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200 articles
DeepTutor: Towards Agentic Personalized Tutoring
arXiv:2604.26962v3 Announce Type: replace Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs). However, current LLMs rely on static pre-training knowledge and lack adaptation to individual learners, while existing RAG systems fall short in delivering personalized, guided feedback. To bridge this gap, we present DeepTutor, a fully open-source agentic framework that unifies citation-grounded problem tutoring with difficulty-calibrated question generation. A hybrid personalization engine couples static knowledge grounding with dynamic learner memory, continuously adapting each interaction to the student's evolving needs. The same personalization substrate further extends to adaptive learning workflows, interactive books, and proactive multi-channel tutoring agents. To evaluate personalized tutoring, we introduce TutorBench, an interactive benchmark incorporating customized learner profiles grounded in university-level curricula across five domains. We further propose an LLM-based first-person interactive evaluation protocol that conducts assessments via a profile-driven student simulator. Complementary evaluations on established benchmarks, supported by human-alignment and ablation studies, confirm the framework's robustness and general utility. Results show that DeepTutor improves personalized metrics by 10.8\% on average and strengthens general agentic reasoning across five backbone models by 29.4\%.
The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy
arXiv:2607.05411v1 Announce Type: new Abstract: Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies. In response, they are introducing professional development programs and embedding GenAI skills within student curricula. However, current educational frameworks typically assume a linear progression of GenAI literacy, implying that foundational technical understanding must precede creative application. This paper challenges such an assumption through a psychometric analysis of a taxonomy-based self-assessment instrument (n = 158). We applied Rasch measurement theory and Guttman ordering to map the latent perceived order of difficulty of GenAI skills across students, academics, and professional staff. Results reveal a fundamental divergence in perceived competence profiles: while academics follow a more traditional linear path, students exhibit an "inverted" profile, frequently mastering high-level creation tasks before acquiring foundational conceptual understanding. Furthermore, the correlation of skill difficulty between students and academics was weak (r = 0.188). We argue that this "skill bypass" creates a fragile sense of fluency, where high self-efficacy in prompting masks low literacy in AI mechanics. These findings challenge the "one-size-fits-all" curricula and provide the empirical basis for diagnostic-driven, modular interventions that foster genuine human-AI synergy.
AI didn’t break higher education—It exposed the credential trap | Fortune
As tuition soars past crisis levels and AI reshapes the classroom, students are rationally optimizing for diplomas over discovery. Call it the degree trap.
Beyond Detection: Redesigning Assessment and Governande of Generative AI at the Universidad Polit\'ecnica de Madrid (UPM)
arXiv:2607.01255v1 Announce Type: new Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far, the dominant reaction has been defensive, this is, most institutions frame the debate around AI detection, plagiarism, academic integrity and a presumed drop in student effort, prioritizing basic training for academic staff over students. Other group of pioneering universities is doing the opposite, pursuing deeper adoption, and assuming that any policy built on prevention or sanction will not hold. This paper sides with that second view. Obsessing about detection is a dead end, since generated text is increasingly hard to distinguish from human writing, and detectors still misfire too often to be trusted. What universities need instead is a coordinated effort to set clear, course-by-course rules for GenAI use, redesign assessment toward authentic and interdisciplinary assessment that fosters critical thinking and learner autonomy, and build a serious AI-literacy programme that treats students as critical co-creators rather than passive users. The challenge, though, is not only pedagogical. Adoption at university scale also raises organisational, technical, operational, legal and economic questions that have to be solved together. In this context, the Universidad Polit\'ecnica de Madrid (UPM) is developing a strategic and sustainable AI policy and adoption framework structured around six dimensions, in which AI functions as an enabler of student autonomy and pedagogical innovation rather than as a threat to be policed.
Universities Embrace AI: Transforming Education with Integrated Curricula and Innovative Assessments
Universities are actively integrating AI into curricula, assessments, and research, moving away from traditional exams to embrace AI-assisted learning and evaluation frameworks. Stakeholders emphasize the need for clear policies and educator training to manage academic integrity and effectively harness AI's potential in fostering critical thinking and creative problem-solving.
OPINION | India’s AI jobs are booming: Can our graduates do the work or only talk about it? - The Week
While employers seek AI talent, a stark reality is that many graduates, despite having degrees, lack the hands-on skills to be deployable
How early AI education can shift learning towards future-ready skills and career preparedness
Impact Of AI On Learning: Explore how early AI education can transform learning experiences, build essential skills, and prepare students for future careers in an AI-driven world.
Report: Lifelong learning must change for AI to realise long-term potential
According to the research, as structural issues begin to limit growth, business leaders are urging policymakers to align strategies and revamp workforce development. Read more: Report: Lifelong learning must change for AI to realise long-term potential
Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming
arXiv:2607.00211v1 Announce Type: new Abstract: Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies. This study introduces the conceptual framework of Epistemic AI Literacy (EAIL), reframing AI literacy as a process-oriented epistemic phenomenon that emerges through dynamic human-AI interactions across different domains. Drawing on the AIR (epistemic aims, ideals and reliable epistemic processes) framework, this study examines how epistemic aims and epistemic processes are enacted in GenAI-supported co-programming activities and explores scalable approaches for operationalizing these constructs in interaction data. Using a large dialogue dataset of human-AI co-programming, this study identifies observable dimensions of epistemic aims (i.e., mastery-oriented aims) and epistemic processes (i.e., outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification). The results reveal a prevalent lack of EAIL, with 78.8% of student-GenAI interactions relying on non-mastery-oriented aims and less reliable epistemic strategies like outsourcing and verification-seeking. Conversely, only 11.1% of interactions showed high epistemic engagement, where mastery-oriented aims were coupled with advanced epistemic strategies like epistemic justification in a more reliable epistemic process.
A Penny for Your Prompts: Experiments Detecting and Mitigating LLM Usage by Survey Respondents
arXiv:2607.00403v1 Announce Type: cross Abstract: Large language models are increasingly used by participants on crowdsourcing platforms when responding to surveys, potentially undermining the validity of collected data. Our study aims to quantify the prevalence of this behavior and investigate methods to detect and prevent it. In a series of surveys (N = 250), we examined conditions such as platform choice, survey length, requests not to use AI, and disabling copy-paste functionality. We were able to identify distinct characteristics of LLM-assisted responses and found that their frequency varied widely, from under 10% on Prolific to over 80% on Mechanical Turk. Mitigation measures reduced LLM usage but did not necessarily improve data quality. No participants employed browser-use agents at the time of our survey, but we report on our own detection experiments. We recommend that researchers actively screen survey responses for LLM usage by recording and analyzing keystroke data and crafting instructions and questions aimed at AI.
CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education
arXiv:2607.00140v1 Announce Type: new Abstract: As computing education expands beyond traditional programming into operational domains such as systems administration and command-line environments, existing pedagogical frameworks struggle to capture a dimension that is critical in these contexts: the real-world consequences of learner actions. Existing cognitive taxonomies classify learning objectives by mental operations but do not account for system impact, leaving a critical gap in command-line education where conceptually simple commands can have severe consequences. This work presents CogTax, a four-level cognitive taxonomy that integrates two dimensions: cognitive complexity, derived from Bloom's Revised Taxonomy, and operational impact, which distinguishes observational, reversible, structural, and administrative operations. The four progressive levels range from safe read-only inspection to advanced system management requiring integration of multiple abstract models. Then, the taxonomy level is defined as the maximum of these dimensions, ensuring that both conceptual understanding and operational awareness are addressed. CogTax gives instructors a principled framework for sequencing course material and calibrating assessment difficulty, and gives students an explicit reference for self-assessment and gap identification. To demonstrate that taxonomy levels are automatically assignable, making the framework scalable without manual expert annotation, a classifier that combines syntactic representations derived from abstract syntax trees with semantic embeddings is trained. Evaluated on 585 expert-annotated Linux/bash commands, this combined approach achieves 89% accuracy, outperforming either representation alone, and demonstrates cross-language extensibility through structural equivalences across command languages.
Teaching with AI: The case for clear rules in higher education - The Hindu
Explore the need for clear AI guidelines in higher education to enhance student skills and address academic integrity concerns.
AI Skills Gap Persists Despite 80% Student Adoption - Telecom Review Americas
Pearson and Amazon Web Services (AWS) released new research showing that while the United States is a leader in AI innovation, there is more opportunity to prepare students with the skills needed to support an AI-ready workforce. According to the research, employers place greater value on higher ...
UNICEF urges child-focused AI governance | Digital Watch Observatory
AI governance should prioritise children’s safety, privacy and rights, UNICEF said.
Connect Education To Jobs And Create An AI Workforce Transition Plan
An AI workforce transition needs more than retraining. P-TECH shows how education, employers and credentials can connect workers to jobs reshaped by AI.
European quantum and AI academies to build critical tech workforce
Three academies have been designed to address challenges of sovereignty and competitiveness in quantum, AI and virtual worlds through a coordinated strategy. Read more: European quantum and AI academies to build critical tech workforce
AI in Education: Students Must Learn to Be Human, Says Saikat Majumdar - Frontline
As AI transforms learning and work, Saikat Majumdar argues ethics, inequality, and human relationships—not technical skills alone—will define the future. Read why.
Toward AI-Resilient Assessment in Computer Science Courses in an AI-Native World
arXiv:2606.30655v1 Announce Type: new Abstract: AI-native course assessments in senior computer science courses and related fields should grade students by \emph{AI-resilient skill}: the ability to achieve outcomes beyond a strong AI baseline. Such assessments should allow students to use AI freely, while reducing the extent to which greater private AI budget or more intensive AI use, by itself, becomes a grading advantage. This paper proposes a minimal formal framework for this goal. The framework specifies a real task, an executable evaluator, a declared AI-native Pareto frontier, and a grading rule based on Pareto surplus. The central claim is simple: Pareto surplus provides a measurable, protocol-relative certificate that a submitted artifact achieves a tradeoff not already supplied by the declared AI baseline, and grading by this surplus is AI-resilient with respect to that baseline. Interpreting surplus as evidence of student skill requires the surrounding assessment protocol--for example, design reports, ablations, prompt traces, oral checks, or reproducibility explanations--but the grading certificate itself is behavioral and executable. The framework is then extended to practical complications, including self-improving AI loops, budget neutrality, server-mediated feedback, and prompt-based red teaming. As a concrete instantiation, we describe an AI-resilient approximate-membership assignment centered on Bloom filters for COMP 480/580 at Rice University, designed to test whether students can improve beyond AI-generated implementations.
ELEVATE: Designing Human-Centered GenAI Virtual Tutors for Scalable and Inclusive Education
arXiv:2606.30662v1 Announce Type: new Abstract: The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while intensifying ethical debate about its adoption. To date, the dominant paradigm remains cloud-based and text-only chatbot: a centralized service that offers limited pedagogical control, weak transparency over knowledge sources, and non-trivial risks for privacy and regulatory compliance. This model also presumes continuous connectivity and recurring API costs, creating structural barriers for many institutions, reinforcing existing digital divides. At the same time, educational interaction with LLM can benefit from multimodal cues and embodied presence, requiring interfaces that move beyond text-only tutoring. In this work, we propose ELEVATE (Efficient LLM Education with Virtual Avatar Teaching Engine), a framework to develop efficient GenAI-driven avatar tutors governed by epistemic infrastructures. ELEVATE integrates LLM-driven dialogue with embodied 3D avatars for multimodal interaction and adopts a local-first execution model enabling deployment on consumer-grade hardware. The framework formalizes a three-stratum design that separates (i) a student-facing virtual avatar interaction layer, (ii) a local GenAI execution and multimodal synthesis core, and (iii) a teacher-facing governance layer. We implemented and evaluated a working prototype deployed in a real-world educational curriculum. The system runs on standard PCs and smartphones, and we provide system-level performance evidence to show responsive interaction under realistic hardware constraints. Finally, we discuss sociotechnical and pedagogical implications for responsible adoption, positioning ELEVATE as a scalable pathway for privacy-preserving and inclusive GenAI tutoring across heterogeneous school environments.
Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework
arXiv:2606.30650v1 Announce Type: new Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students. We introduce a synthetic benchmark and decision-support framework for qualified educational capacity planning. The model is a stylized single-institution service system with heterogeneous support-demand categories, backlog-only dynamics, continuous preparation states with hard threshold qualification and decay, and capacity-consuming training. The benchmark includes seed-controlled scenarios for announced and surprise new support categories, staff absences, and demand surges; exact feasibility discipline; declared per-policy information sets; requalification and greenfield-qualification counters; access-dispersion metrics; replay checksums; and paired statistics. We compare service-only, reactive, static-insurance, water-filling, and rolling-horizon mixed-integer controllers, with an attribution chain separating service planning, qualification maintenance, and acquisition, plus a perfect-foresight reference. The central result is a regime map governed by whether a newly required qualification can be acquired within the controller's reaction reach. When it can, the closed-loop controller wins across the core and adversarial suites, with value concentrated in just-in-time qualification acquisition. When the training lag exceeds the horizon, lean static insurance wins structurally, and a reactive trainer that starts after onset can be worse than no training. Backlog perishability shifts this boundary without erasing either regime. EduCapacity Studio reproduces exported scenarios bit-for-bit. All evidence is stylized and synthetic; the framework makes no claims about real student outcomes, compliance, or individual placements.
Pearson and AWS Research Reveals Gap Between Student AI Use and Workplace Readiness
New research from Pearson and Amazon Web Services has found that while AI use among US college students is widespread, a significant disconnect remains
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
arXiv:2606.28749v1 Announce Type: new Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them. Existing instruments assess reliance solely by frequency of use, a measure that, as this study shows, inadvertently rewards dependence on AI rather than recognizing students' own intellectual contribution. Conducted at a public minority-serving university and grounded in the AI Literacy Framework, Expectancy-Value Theory, and Biggs's Presage-Process-Product model, the study drew on 382 undergraduates, 14 interviews, and 396 open-ended survey responses. Four distinct reliance types were identified and confirmed: Strategic (34.3%), Instrumental (30.9%), Dialogic (30.4%), and Dependent (4.5%). Students' value and cost beliefs predicted the intensity of their reliance on LLMs, whereas their AI literacy predicted the type of reliance they adopted, indicating that differentiated support is needed. Notably, Strategic users, those who engaged AI most deliberately, scored lowest on standard outcome measures. This pattern reflects a limitation of current instruments, which index AI's contribution rather than writing quality, thereby penalizing students who show the greatest independent thinking. Analysis also revealed an additional group, roughly 13%, who declined to use AI for ethical rather than practical reasons, and who existing frameworks overlook. These findings carry implications for AI literacy programs, the measurement of student learning outcomes, and equitable AI policy at minority-serving institutions.
OpenAI Maps AI’s Impact on EU Jobs — What Students Should Know - TUN
OpenAI's Economic Research team has extended its AI Jobs Transition Framework to the European labor market, categorizing EU occupations by automation risk, growth potential and workflow reorganization. The country-level findings carry real strategic weight for students deciding where to build ...
From Prompting to Epistemic Proactivity: Temporal Trajectories of Student-AI Interaction in Mathematics Learning
arXiv:2606.28472v1 Announce Type: new Abstract: GenAI is increasingly used by students as learning companions, yet little is known about how they use these tools in open-ended learning settings, where the goal is not to complete a specific task but to improve understanding and making progress. This study examined Grade-9 students' dialogue with a general-purpose LLM during mathematics practice, in which students prepared a curriculum-aligned skill for a later assessment. We investigated whether students' interactions revealed forms of epistemically proactive AI use: trajectories in which they strategically use and regulate AI to advance their understanding, and whether these trajectories predicted immediate AI-free performance on the same skill. A total of 112 students worked with a web-based LLM tutor on a mathematical-modeling task; 97 completed both AI-free pre- and post-tests. Student turns were coded for self-regulated learning functions, help-seeking content, and mathematical-modeling activity; three dimensions hypothesized to capture epistemically proactive AI use in this task. Descriptively, students' interactions showed little explicit regulation and mostly involved procedural or conceptual questions. Static summaries of AI use, including whole-session prompt functions, request types, modeling stages, and behavioral diversity, did not predict post-test performance after controlling for prior knowledge. In contrast, temporal indicators were informative: students performed better when their interactions shifted from early to late phases toward a more epistemically proactive balance of conceptual or procedural help-seeking and mathematical work, rather than verification, answer-seeking, or validation. These findings suggest that productive AI-supported learning is better understood as a domain-specific trajectory of epistemic proactivity. We discuss implications for AI tutor design and classroom orchestration.
DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums
arXiv:2606.27619v1 Announce Type: new Abstract: Dyslexic learners increasingly use artificial intelligence (AI) tools to support reading, writing, organisation, and study-related tasks. However, their lived experiences with these tools remain largely underexamined. This paper proposes DysLexLens, a low-resource LLM framework, designed to analyse dyslexic learners experience with AI through online forum discussions. DysLexLens is designed as an end-to-end, evidence-traceable architecture which transforms noisy social media posts into a dictionary-driven corpora, provides knowledge-graph (KG)-based question reasoning, generates verifiable query responses, and enables response evaluation through quantitative and human-grounded assessment. DysLexLens has four key features. First, it employs a dictionary-driven filtering method to construct a more focused Reddit corpus on dyslexia and AI, filtering out noisy and weakly related posts to improve the relevance of data collected from low-resource forum contexts. Second, it integrates LLM-assisted semantic analysis with KG-based query reasoning to uncover meaningful patterns. Third, it has quantitative evaluation metrics (RAGAS and Query Robustness) to measure LLM-generated response performance. Fourth, it provides structured qualitative validation guidelines for assessing response quality, with a specific focus on hallucination and evidence alignment. We demonstrate the effectiveness of DysLexLens using dyslexia-related Reddit forum data and 30 questions. The results show its potential generalisability to other low-resource forum data contexts. DysLexLens, sample data, questions and evaluation results are available at Github to support reproducibility.
Verifiable Geometry Problem Solving: Solver-Driven Autoformalization and Theorem Proposing
arXiv:2606.27926v1 Announce Type: new Abstract: Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor. However, current frameworks suffer from severe bottlenecks in two core stages: autoformalization, which treats multimodal translation as a static task decoupled from downstream solver compatibility, and theorem prediction, where solvers frequently hit a deductive impasse due to fixed rule libraries. To address these, we propose SD-GPS, a solver-driven framework that treats the symbolic solver as an execution oracle throughout both formalization and deduction. First, Solver-Driven Autoformalization unifies supervised formal-language adaptation and solvability-guided reinforcement learning into a single module built on QwenVL3-2B, making executability the central training signal. Second, Verified Theorem Proposing introduces an impasse-aware agent that proposes local auxiliary lemmas from current proof states, ensuring soundness by filtering all proposals through symbolic verification. Empirical evaluations on Geometry3K and PGPS9K demonstrate that SD-GPS consistently outperforms existing MLLM, neural, and neuro-symbolic methods across standard completion, multiple-choice, and cross-modal reference regimes, proving that closing the loop between multimodal perception and symbolic execution significantly improves geometric reasoning, offering profound insights into how neural agents can be grounded by formal systems to achieve verifiable problem-solving capabilities.
Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
arXiv:2606.28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the cognitive processes that make items difficult. We argue that difficulty should be viewed not only as a property of item text, but also as an observable consequence of the problem-solving burden an item induces. Large Reasoning Models (LRMs) offer scalable process evidence through reasoning traces, but such evidence must be structured to support interpretable modeling. To this end, we introduce Epi2Diff (Episode to Difficulty), a framework that maps LRM reasoning traces into cognitively grounded episode sequences. These episodes group trace segments into functional problem-solving states, enabling difficulty to be modeled through reasoning scale, effort allocation, and state transitions. Epi2Diff extracts compact episode-dynamic features and combines them with semantic item representations for human difficulty prediction. Experiments on four real-world human difficulty datasets show that Epi2Diff consistently outperforms strong baselines, including fine-tuned small language models, LLM in-context learning, and supervised LLM adaptation. On SAT-derived classification benchmarks, Epi2Diff achieves an 8.1% average relative gain over supervised LLM fine-tuning baselines. Further analyses show that harder items induce more effortful, iterative, and implementation-centered episode dynamics, rather than merely longer responses. These results demonstrate that cognitive episodes in LRM reasoning traces provide a predictive and interpretable process representation for human item difficulty, offering a new lens for educational measurement with reasoning models.
AI Reskilling 2026: Who's Actually Paying to Retrain Millions? - Memeburn
Here is the stat that matters more ... their workforce urgently needs AI skills. Yet only 6% have started reskilling in any meaningful way. That gap isn’t accidental. Training programs are slow, expensive, and hard to measure. AI tools, by contrast, show immediate ROI on balance sheets. As a result, the economics favor buying the software over training the person — every time. The Brookings Institution made this clear: well-funded retraining programs often ...
David Autor named head of the Department of Economics
David Autor has been appointed as the head of the Department of Economics at MIT.
Strategic Initiatives in AI Workforce Development and Future Skills Training
Collaboration between industry leaders and workforce development organizations aims to address the evolving skill requirements of the AI-driven economy. These initiatives focus on bridging the gap between current labor capabilities and future job market needs.
Studies: AI skills shield workers from layoffs, West Virginia itself less susceptible | WV News | wvnews.com
CLARKSBURG, W.Va. — The rise of artificial intelligence has also given rise to Luddite-esque fears that the technology will displace and even replace workers in the future.
The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning
arXiv:2606.26181v1 Announce Type: new Abstract: With AI advancing fast, educators face a dilemma: allow the tool or ban it. Conflicting evidence that it both helps and hurts learning only deepens the confusion. The allow-or-ban framing is a false dichotomy; the relevant design question is placement. Used well, AI can scale feedback, examples, practice, and individualized support. Used poorly, it replaces the cognitive work that learning requires and leaves an illusion of learning: a confident sense of mastery that collapses on the unaided task. The strongest causal evidence shows the outcome flips on design: an unguarded AI helper left high-school students about 17% worse on an unaided exam than peers with no tool at all, while the same model rebuilt to withhold answers erased the harm, and a well-engineered tutor roughly doubled learning. We give educators one graspable frame for placing the tool. A new idea is learned through six moves, in order: Prime, Probe, Point, Attach, Strengthen, and Test. Secure the first hard attempt and the final unaided check, scaffold with guarded AI in between, and one diagnostic carries the frame: if letting AI in makes the task feel effortless, it is in the wrong place. To make it usable, we map classical teaching moves and AI-supported interventions to each step. Together, the six-move model, the placement rule, and the intervention menu provide a practical foundation for lesson and course redesign in the age of AI.
The Tilted Playing Field for Women in Science
arXiv:2606.26469v1 Announce Type: new Abstract: Institutional prestige shapes access to resources, visibility, and collaboration opportunities in science. Yet whether prestige benefits researchers equally, and how it relates to differences in scientific productivity and collaboration, remains unclear. Here, we quantify prestige advantage as the relative likelihood that researchers at higher-ranked institutions have more collaborators and produce more high-impact papers compared to their lower-ranked peers. Analyzing nearly 5 million papers by 6.5 million authors across more than 65,000 institutions, we present a distributional, tail-sensitive framework to compare prestige advantage across groups. We find that the association between prestige and scientific achievement differs systematically by gender. While both men and women benefit from prestige, the returns are not gender-neutral: women experience comparable advantages only at the most elite institutions, whereas men retain persistent advantages across the broader hierarchy, with disparities widening at higher levels of achievement. Prestige advantage also grows nonlinearly, disproportionately benefiting authors at the most elite institutions. These differences align with collaboration patterns: women's networks are more locally clustered and focused on their own institution, while men collaborate more broadly across institutional strata. Together, these findings reveal a tilted playing field in science: one where prestige amplifies success unevenly and network structure shapes who can access its benefits.
RAISE US launches AI workforce initiative | ETIH EdTech News — EdTech Innovation Hub
AI skills and workforce training gain more than $500 million in commitments as Gina Raimondo and Eric Holcomb launch RAISE US. ETIH edtech news covers state pilots, apprenticeships, worker transitions, and support from Microsoft, Amazon, Anthropic, and the OpenAI Foundation.
The New Push to Ready Millions for AI Career Upheaval
A coalition of employers and state governments says it is developing a sweeping strategy to help workers respond to the AI age.
Gen Z graduates are blaming AI for their unemployment woes when they should be looking somewhere else
While AI companies sow anxiety around their technologies, there’s evidence elsewhere of different economic factors actually driving employment challenges.
LLM Performance on a Real, Double-Marked GCSE Benchmark
arXiv:2606.24973v1 Announce Type: cross Abstract: We introduce a dataset of 32,534 double-marked real student responses to GCSE mock exams (GCSEs are the UK's national exams, taken at age ~16), spanning 328 questions across five subjects and including handwritten work. We test whether off-the-shelf large language models agree with examiners as closely as the two examiners agree with each other. We find that models overwhelmingly agree well with the examiner consensus across subjects, with the top performing models agreeing more closely with examiners than examiners agree with each other. Models achieve high scores for subjective tasks like English essay marking, as well as handling complex and messy handwritten Maths paper scripts. Agreement is uniform near the examiner line, and not massively discriminated by model size, providing cost-effective automated marking solutions.
Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers
arXiv:2606.24099v1 Announce Type: new Abstract: Algorithms have become central to scientific research in the era of artificial intelligence (AI). Although algorithm mentions in papers are often used to indicate popularity and influence, existing studies usually evaluate individual algorithms in isolation and pay limited attention to the collective influence formed through their interconnections. This study constructs large-scale algorithm co-occurrence networks in natural language processing (NLP) based on the full text of academic papers and investigates algorithm influence from a network perspective. Using deep learning models, we extract algorithm entities and build overall, cumulative, and annual co-occurrence networks. We analyze their structural characteristics and apply multiple centrality measures to assess the group influence of algorithms across the whole field and over time. The results show that algorithm networks display typical features of complex networks, with increasingly dense connections developing over approximately two decades. Classic, high-performing algorithms and those located at the intersections of different research periods tend to have high popularity, control, centrality, and balanced influence. When the influence of an algorithm declines, it usually loses its core network position first, followed by weaker associations with other algorithms. This study is the first large-scale analysis of algorithm co-occurrence networks. Covering more than four decades of academic publications, it provides a temporal and structural view of algorithm influence and offers a foundation for future research on networks linking algorithms, scholars, and tasks.
A new $500 million push to retrain workers for an AI-driven future | The Independent
And unlike many other quality news ... and analysis with paywalls. We believe quality journalism should be available to everyone, paid for by those who can afford it.Your support makes all the difference.Read more · The United States is hurtling towards an artificial intelligence-driven future, largely unprepared for the potential for widespread job displacement. While some warn of "doomsday scenarios" reminiscent of science fiction, proponents argue AI will create ...
The AI-Tutor Revolution That Wasn’t - The Atlantic
“We are social beings,” Mary ... current educational technology practitioner and researcher, told us. “We want to learn with and from other people.” Burns points to the learning loss during the coronavirus pandemic as evidence of what happens when we underestimate the value of learning communally. When students were isolated at home, without peers and often beyond the reach of teachers, “we saw a psychic break,” she said. Training AI in the skills of the best ...
‘More relevant than making fires’: Explorer Scouts launch badges for AI and digital age
Content creation and online safety among new topics for 14- to 18-year-olds – but tweaks may be needed when social media ban comes in Scouts are introducing badges in content creation, digital communication and online safety after consulting nearly 3,000 teenagers who said they wanted skills to help them navigate a world increasingly shaped by AI, social media and digital technology. The new Explorer Scout badges, part of the Scout movement’s first major overhaul in almost 25 years, will require 14- to 18-year olds to explore how digital communities shape opinion, create online campaigns, investigate digital footprints and design toolkits to help others stay safe online. Continue reading...
Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
arXiv:2506.04435v2 Announce Type: replace-cross Abstract: Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for combining name- and perception-based demographic labels by maximizing alignment with self-reported demographics from a survey of faculty from our census. We find that women and individuals with minoritized race identities are less central in the computer science coauthorship network, implying worse access to and ability to spread information. Centrality is also highly correlated with prestige, such that faculty in top-ranked departments are at the core and those in low-ranked departments are in the peripheries of the computer science coauthorship network. We show that these disparities can be mitigated using simulated edge interventions, interpreted as facilitated collaborations. Our intervention increases the centrality of target individuals, chosen independently of the network structure, by linking them with researchers from highly ranked institutions. When applied to scholars during their Ph.D., the intervention also improves the predicted rank of their placement institution in the academic job market. This work was guided by an ameliorative approach: uncovering social inequities in order to address them. By targeting scholars for intervention based on institutional prestige, we are able to improve their centrality in the coauthorship network that plays a key role in job placement and longer-term academic success.
Cross-Subject Predictive Validity for Learning Outcomes of Delayed Start Behavior
arXiv:2606.25308v1 Announce Type: new Abstract: Behavioral detectors provide valuable insights into learner motivation and self-regulation. Among these, delayed start, a new session-level detector, has shown great promise as a valid behavioral measure that generalizes well across systems. In this paper, we examine cross-subject predictive validity of delayed start behavior. Using iReady data from 711 grade 7 students, we find delayed starts during Math practice are predictive of standardized test performance in both Math ($\beta$=.07 SD, p=.02) and English ($\beta$=.10 SD, p=13 minutes average delay, 20% of students). Relative to students in neither sub-group, early starters experienced greater growth (Math $\beta$=.11 SD, p=.07; ELA $\beta$=.15 SD, p=.02), while chronic delayers had the opposite trends (Math $\beta$=-.13 SD, p=0.05; ELA $\beta$=-.11 SD, p=0.11). Session-level measures provide a new opportunity for content-independent detectors, adding a behavioral component to the traditional usage and progress based on student engagement with content. This work aims to bridge education research with classroom practice by developing interpretable measures that align with behavioral cues teachers already use during classwork sessions to monitor and support students.
Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities
arXiv:2606.25320v1 Announce Type: cross Abstract: Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain. Using academic papers from LIS published between 1990 and 2022, four key categories of data-driven method entities are automatically extracted: algorithms and models, data resources, software and tools, and metrics. Based on these entities, the study examines the evolution of LIS research methods from three dimensions: the characteristics of research method entities over time, their evolution within different research topics, and the evolutionary features of research method entities across various research methods. The findings highlight data resources as a pivotal driver of methodological evolution in LIS, revealing a cyclical pattern of "emergence-stability/practical application" in the development of research methods within the field.
AI Can’t Fix the Student-Motivation Problem
It turns out bots aren’t great teachers.
Why the AI Job Displacement News May Not Be What HR Leaders Think
Discover what new SHRM research presented at SHRM26 reveals about AI and job displacement trends, and why most jobs are being transformed rather than replaced.
India to Lead Human Skills Economy Amid AI Adoption
India is poised to lead the human skills economy with the world’s youngest workforce and highest AI adoption rate, according to a new report.
Is Higher Team Gender Diversity Correlated with Better Scientific Impact?
arXiv:2606.24098v1 Announce Type: cross Abstract: Collaborative research involving scholars of various genders constitutes a prominent theme in scientific research that has garnered substantial attention. While several studies have investigated the connection between gender-specific collaboration patterns and the scientific impact of paper, the specific gender diversity factors that contribute to enhanced scientific impact remain largely unexplored. In this study, we analyze the correlation between gender diversity and the scientific impact of papers using the examples of Natural Language Processing (NLP) and Library and Information Science (LIS) domains. Our findings reveal three key observations: First, significant gender disparities exist in both NLP and LIS domains, with underrepresentation of female scholars. The gender disparity is more pronounced in the NLP domain compared to the LIS domain. Second, based on papers from the NLP and LIS domains, we find that papers with different gender compositions achieve varying numbers of citations, with mixed-gender collaborations gradually obtaining higher average citation counts compared to same-gender collaborations. Lastly, there is an inverted U-shaped relationship between the gender diversity of paper collaborations and the number of citations received by those papers. Based on the most impactful gender diversity calculations, the ideal gender ratio for NLP and LIS teams within a range where one gender constitutes 5% to 15% of the total number of authors. This paper contributes to the exploration of the most impactful gender diversity in collaborative research and offers insights to guide more effective scientific paper collaboration.
Context-Aware Prediction of Student Quiz Performance with Multimodal Textbook Features
arXiv:2606.24770v1 Announce Type: new Abstract: Educational platforms often predict student performance from prior interactions, but the assessment content itself also varies in linguistic and visual complexity. This paper studies whether lightweight content features extracted from CourseKata chapter-review questions improve prediction of end-of-chapter quiz scores beyond a student's average prior exercise performance. The study combines 2023 CourseKata student response data with chapter-level text features from review-question wording and image features from textbook visuals. Across 4,742 student-chapter observations from 562 class-student IDs, adding content features improves student-grouped five-fold quiz prediction performance by 9.1% relative to a prior-performance baseline. In leave-chapter-out validation, text features reduce prediction error relative to the baseline, while image-containing models have higher error. This paper suggests that a context-aware model adds useful signal about the text and visual features of questions to better predict student quiz performance compared with using past student performance alone.
AI in the classroom prompts tide of concern from US parents and experts
While tech companies and Trump have been pushing teachers to use AI in the classroom, many argue that there is little evidence that it would actually help children In October, Kelly Clancy’s son received an assignment in sixth grade at a middle school in Brooklyn, New York, to create a science experiment and then ask Google Gemini, an artificial intelligence chatbot, for feedback, she said. Clancy, who has three children in New York City public schools, told the teacher that the bot “is something that just teaches kids that they can have machines do the thinking for them”, instead of suggesting: “Let’s talk to your partners. What about the science experiment could you improve?” Continue reading...
Skilled Workers Are Finally Gaining Ground. AI Will Decide Whether They Keep It.
WASHINGTON, June 23, 2026 /PRNewswire/ ... (AI) reshaping how Americans get hired will open doors for skilled workers or quietly wall them off. 2026's State of the Paper Ceiling report makes the case that at its core, the labor market is infrastructure. A built environment of signals and pathways, signals are the gatekeepers that decide who gets seen and let through. For example, the job requirements employers write and ...
Sharing a love for calculus
The national conversation about the value of education is currently dominated by speculation about the risks and positive potential of AI. Whatever your own perspective on that debate, I hope you’ll be glad to know that MIT is also working on a deeply important but comparatively old-fashioned challenge: American high school students’ startlingly uneven access…
FEU Tech Partners with OpenAI to Become Philippines' First AI-Native University
FEU Tech is integrating OpenAI technology across its academic and administrative functions to prepare students for an AI-driven economy.
Chinese universities cut humanities to expand AI programs | Let's Data Science
Between 2021 and 2025, Chinese universities revoked or suspended **12,200** undergraduate programs and introduced **10,200** new ones, according to Ministry of Education data cited by Xinhua and reported by VnExpress. A May 2026 survey of 70 universities found reductions concentrated in ...
Opinion | New York City politicians push a moratorium on AI in schools - The Washington Post
Forward-thinking educators are finding creative ways to use artificial intelligence to help students. Meanwhile, the majority of New York’s City Council is pressing Mayor Zohran Mamdani (D) to “immediately pause” any use of AI in schools.
Beware a two-speed workforce: AI skills are the new great divide - Entrepreneur United Kingdom - Entrepreneur United Kingdom
AI is transforming work faster than expected. Without investment in skills and training, the UK risks creating a two-speed workforce and economy.
Six skills that can save you even when AI comes for your job - CNBC TV18
As AI changes the future of work, these six essential skills can help professionals stay relevant, improve career growth and remain valuable in an AI-driven workplace.
The Potential for AI-Driven Acceleration in Academic Research and Knowledge Synthesis
Advanced models demonstrate the capability to critique, update, and extend historical academic research. This shift suggests a significant productivity multiplier for knowledge work if applied to large-scale scholarly archives.
Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023
arXiv:2606.19469v1 Announce Type: new Abstract: Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable, reproducible way to measure how completely they cover the current guidelines and how that coverage shifts when the guidelines are restructured. We address this with a human-in-the-loop pipeline that measures a program's coverage of an external body of knowledge, applied longitudinally to one accredited BSc in Computer Science against Computer Science Curricula 2013 (CS2013) and 2023 (CS2023). The pipeline represents the program and each guideline as structured corpora, generates candidate course-to-knowledge-unit matches by semantic retrieval, and confirms them through human judgment under an explicit coverage definition. Of seven benchmarked retrievers, a reciprocal-rank-fusion ensemble was strongest, and a reputed long-context model underperformed a small sentence model, so retriever choice must be measured. Both maps were validated by an independent second rater (Cohen's kappa 0.64 for CS2023, 0.69 for CS2013). The program covers 49.7% of CS2023 and 50.9% of CS2013 knowledge units, near-constant across a decade. Extending the same retrieve-then-confirm design to competency articulation and cognitive depth shows that the program articulates the competency for ~88% of covered units under each guideline, yet delivers it at the recommended depth for 76% of present units under CS2023 against 95% under CS2013, a gap reflecting the newer guideline's raised expectations, not the program. The longitudinal comparison separates persistent structural gaps (parallel and distributed computing, foundations of programming languages, systems fundamentals), uncovered against both guidelines and ABET, from differences that reflect the standard's evolution. The instrument is reusable and available from the authors on request.
Norway imposes near ban on AI in elementary school
Norway has implemented strict restrictions on the use of artificial intelligence tools within elementary school settings.
How Preply combines AI and human tutors to personalize learning
Preply utilizes OpenAI's API to transcribe and analyze tutoring sessions, providing personalized feedback to both learners and tutors.
AI-Driven Assessment of Human Tutors: Linking Training Performance to Real-Life Practice
arXiv:2606.18617v1 Announce Type: new Abstract: There exist numerous tutor training platforms. However, few provide AI-driven training and evaluation for human tutors based on real-life performance. We present an AI-driven system that assesses both open responses during training and authentic real-life tutoring. Unlike platforms that only assess learning through online training or simulations, our system utilizes Generative AI (Gemini-2.5-pro) to analyze transcriptions of authentic tutoring, measuring the transfer of tutor skills to real-life application. Human tutors instructing students remotely in math (N=86) completed six scenario-based lessons, averaging a significant 7.4% learning gain. Using mixed-effects models across 405 session-to-lesson pairs, we found that training performance significantly predicted real-life transcript scores with an effect size of 0.25 SD. Model comparison (AIC/BIC) indicated averaging open response and multiple choice performance during training predicted real-life tutor performance best, although open responses were comparatively more predictive. Exploratory analysis showed that after training, tutors were significantly more likely to encounter pedagogical opportunities to apply their skills (61.1% to 68.9%) and demonstrated higher execution quality within those opportunities (65.5% to 68.1%). Interrupted time series analysis suggested that these tutor improvements were part of a gradual trend over time rather than an immediate intervention effect of training. We illustrate an AI-driven method to link tutor training with real-life assessment. In doing so, we contribute open datasets, AI prompts, and scoring rubrics to support transparency and reproducibility.
Confident yet Concerned: Inconsistencies in Computing Students' Attitudes on Cybersecurity
arXiv:2606.18541v1 Announce Type: cross Abstract: Today's young adults are most immersed in technology, leading in feelings of powerlessness in managing online privacy across many platforms, and particularly susceptible to phishing attacks. This raises questions about their general, wide-ranging attitudes towards and management of cybersecurity. How do young, tech-savvy adults approach cybersecurity? We seek a better understanding of their cybersecurity knowledge, attitudes and experiences, in particular in addressing deceptive online communications. We surveyed a group of `lead users': computing university students (n = 236). By combining thematic analysis of open-ended responses with quantitative data, we provide insights into their experiences and perceptions. While students demonstrate reasonable cybersecurity awareness, their cybersecurity experiences vary, and inconsistencies exist around their practices, perceptions of responsibility, and support structures. Findings also reveal four key thematic tensions: 1) Computing students are knowledgeable yet have persistent incorrect beliefs, 2) They learn more about keeping safe from sources outside the classroom, 3) They have limited assistance and have fallen victim to cybercrime, and 4) Many are confident, yet others are concerned about their own safety and responsibility. Through cluster analysis of attitudes, we identify two groups, with one feeling less prepared, less confident, yet expressing a desire to learn more. Established measures of intentions and objective knowledge were correlated to preparedness. Self-efficacy correlated to confidence and predicted cluster membership.
Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
arXiv:2606.18548v1 Announce Type: new Abstract: Adaptive AI ethics instruction in graduate research training benefits from intake measures that reflect differences in prior LLM experience. Prior coursework or workshop attendance is an obvious candidate, but it is not clear whether it is associated with pre-instruction ratings on key AI perception items. We compare three candidate intake features, self-reported usage frequency, self-rated LLM familiarity, and prior AI education, across five baseline perception outcomes in 93 bioscience graduate and postdoctoral trainees enrolled in a required research ethics course. Usage frequency shows Holm-corrected associations with all five outcomes, self-rated familiarity with three, and prior AI education with none. A threshold-like pattern at the lower end of the scale is most visible for training interest and accuracy trust rather than appearing as a uniform gradient across all five outcomes. In a short intake survey, reported LLM use is more consistently associated with these perceptions than prior coursework or workshops, with self-rated familiarity serving as a secondary indicator. These results suggest that simple pre-instruction behavioral signals can inform lightweight intake profiling for adaptive AI ethics education.
Student cheating now impossible to detect
New reports suggest that the rise of advanced AI tools has made it increasingly difficult for educators to detect student cheating.
Reshaping how companies operate: AI’s impact on the workplace - CKGSB Knowledge
As AI develops rapidly, much discussion around the future of employment is taking place. Lumii AI’s Mei Yuxiang discusses the impacts Much of the public discussion around artificial intelligence focuses on a single question: how many jobs will AI replace? But that framing misses the larger ...
Unicef backs Irish NGO Camara Education with $2.56m
Camara Education Ethiopia will support the roll-out of 115 AI-powered digital learning hubs across Ethiopia. Read more: Unicef backs Irish NGO Camara Education with $2.56m
Artificial intelligence in education - AI | UNESCO
UNESCO AI in Education guides the ethical use of artificial intelligence to enhance learning, teaching, and assessment globally.
Self-Efficacy and Favorability Shape Learning from Tutoring Systems and Paper Practice
arXiv:2606.17470v1 Announce Type: new Abstract: Motivational factors such as self-efficacy and how favorably students feel toward practice play a crucial role in shaping learning, particularly in technology-supported environments. Yet, educational interventions often overlook how these factors interact with practice format. This paper examines the influence of self-efficacy and favorability on learning outcomes across two common practice formats: paper-based and system-based tutoring practice. Using a counterbalanced within-subject design with matched problem sets, we isolate the effect of practice format while modeling motivational differences. Results indicate that students with lower baseline self-efficacy achieved greater learning gains regardless of practice format. Among students with lower baseline self-efficacy, greater favorability toward the tutor was associated with greater learning gains during tutor practice, whereas the pattern differed in paper-based practice. Intelligent Tutoring System (ITS)-based practice did not significantly improve post-training self-efficacy relative to paper-based methods. These findings underscore the potential value of tailoring practice format to students' motivational profiles, as the benefits of tutor- and paper-based practice varied with baseline self-efficacy and favorability. They lay the groundwork for future research on how instructional formats can be aligned more effectively with learners' motivational needs.
LearnOpt: Recovering the Latent Cognitive Structure of Standardized Examinations via Knowledge Graphs and Constrained Optimization
arXiv:2606.15349v1 Announce Type: new Abstract: Standardized examinations are typically treated as uniform syllabus coverage problems. We argue they are better understood as adversarial systems with stable latent cognitive structures diverging systematically from official syllabi. We introduce LearnOpt, which recovers this structure from historical question papers and generates personalized, time-bounded study plans. Applied to nine years of NEET questions (2016-2024, n=1,496), LearnOpt builds an exam knowledge graph from LLM-tagged questions, extracts a five-category latent skill distribution, and formulates study planning as a knapsack-variant optimization over prerequisite-aware subgraphs with Bayesian Knowledge Tracing. Central finding: NEET's latent skill distribution is stable within a syllabus regime (consecutive-year KL divergence 0.004-0.032 for 2016-2021, non-significant under permutation testing) but shifts significantly with NCERT's 2023 syllabus rationalization: pooling 2016-2021 (n=1,072) vs 2023-2024 (n=392) gives KL=0.040 (p=0.0005), with Elimination/Negation questions rising from ~20-29% to ~31-35%. Latent structure, while not permanently stationary, is piecewise stable, with shifts detectable and attributable to curricular events. Within either regime, subject predicts skill profile more strongly than year. An optimization evaluation, using one real and two synthetic mastery profiles, shows the skill-weighted objective produces a modest but real reordering of recommended topics over a mastery-conditioned frequency baseline. Applying the pipeline to JEE Advanced reveals a profile dominated by Multi-concept Integration (80.9% vs. 33.3% for NEET), with a JEE-vs-NEET divergence (KL=0.505) exceeding NEET's largest cross-subject divergence: exam tier shapes latent cognitive structure more than subject, which shapes it more than time within a regime. Code, knowledge graph, and annotated dataset are released publicly.
Gender Differences in AI Literacy Workshop Outcomes and Deepfake Engagement
arXiv:2606.14718v1 Announce Type: new Abstract: As Artificial Intelligence (AI) literacy initiatives expand in K-12 settings, understanding how gender shapes student baseline perceptions, tool-use, and responsiveness to interventions is essential for equitable curriculum design. This study examines gender differences in AI literacy, safety awareness, and STEM career aspirations among Australian secondary students (Years 7, 8, and 10; N(pre) = 199, n(post) = 136) from two co-educational government schools who participated in a one-day AI literacy workshop. Using statistical regression methods controlling for year level and school, we found that pre-workshop, male students reported significantly higher STEM career interest across all three domains (AI, computer science, and engineering), while female students were significantly more likely to use AI for schoolwork and to seek advice from AI tools. Gender-differentiated patterns also emerged in deepfake behaviours: males were significantly more likely to have created or shared deepfake content. Both genders improved in AI knowledge post-intervention, yet females showed a richer profile of gains: wider conceptual understanding, greater confidence, and meaningful increases in AI and CS career interest that partially narrowed the gender STEM gap. These findings highlight the need for gender-responsive AI curricula, particularly deepfake safety education for male students, and demonstrate that even single-day workshops can narrow gender gaps in STEM aspirations and AI confidence.
69% Education leaders flag curriculum-industry gap, 71% see AI shaping future of learning: ETEducation survey
ETEducation White Paper 2026: Exclusive ETEducation survey of 300+ education leaders across 15+ cities reveals employability, industry alignment and AI adoption as key priorities shaping India’s education sector towards 2035.
Improving Capstone Team Outcomes through Dynamic Skill Matching and Preference Alignment
arXiv:2606.15572v1 Announce Type: new Abstract: Team-based projects are a cornerstone of engineering and computing courses, but unstructured team formation often leads to poor project outcomes due to misaligned student interests and inadequate skill coverage. This paper introduces a novel, three-stage methodology for creating effective student teams by integrating student preferences with project skill requirements. In the first stage, students complete a survey to report their project interests and self-assessed skills. Next, a Large Language Model (LLM) analyzes project descriptions to extract the necessary skills for each project's success. Finally, a dynamic assignment algorithm matches students to projects, simultaneously maximizing skill coverage and preference alignment. The algorithm iteratively prioritizes projects with unfulfilled skill needs to optimize team balance. Preliminary evaluations show our approach produces teams with higher skill coverage and better preference satisfaction compared to random or manual assignment approaches. Our approach also overcomes limitations of widely-used tools like CATME Team-Maker, which do not explicitly account for project skill fulfillment. Our findings point toward an effective and customizable strategy for improving student motivation and learning outcomes in project-based courses.
"Stuck in a Spiral": Shame and Guilt as Social Regulators of AI Use in Computing Education
arXiv:2606.14920v1 Announce Type: new Abstract: While prior work has examined patterns of adoption and social norms around AI use, less is known about how emotional factors, such as shame and guilt, shape students use of AI tools. We present an interview study with 19 computing students through a functionalist perspective of shame and guilt, which interprets emotions as social signals that regulate behavior. Our findings show that these emotions regulate when and how students make their use visible, as they engage in hiding behaviors and selective disclosure. Students described shaming themselves, their peers, and even faculty for using AI. Shame and guilt often coexist with continued AI use, creating cycles of reduced agency and moral tension rather than promoting behavior change. Students described feeling tensions between their AI use and their identities as competent, hardworking, or ethical computing students. Students also used language and metaphors of addiction to describe their experiences. These results highlight the need to consider the socio-emotional aspects of AI use, which may be influenced by how AI policies are implemented and enforced. We discuss classroom practices that can foster healthy, open discussion and support responsible AI use.
India’s AI challenge is a workforce skilling challenge | Policy Circle
India’s AI dividend will depend on whether its workforce skilling can be fast enough. #AI #skillling
Skill Premia and Pre-Marital Investments in Marriage Markets
arXiv:2605.10060v3 Announce Type: replace Abstract: I study a decentralized marriage market with search frictions, costly pre-marital skill investments, and non-transferable utility. Despite a fully symmetric environment, asymmetric equilibria -- in which one gender systematically invests more in skills than the other -- can arise. The match payoffs are microfounded through a non-cooperative household game in which spouses allocate time between labor-market work and domestic production. An asymmetric equilibrium becomes available precisely as the high-skill wage rises. Further, the symmetric equilibria can be fragile while the asymmetric ones are not. Thus, rising skill premia may amplify rather than narrow gender gaps in skill acquisition.
The 3 skills most likely to survive AI automation
Perplexity’s Dmitry Shevelenko discusses the company's strategy and identifies the three most durable human skills for the future of work.
Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
arXiv:2606.13684v1 Announce Type: new Abstract: Automatic Bloom's taxonomy classification of assessment questions can substantially reduce instructor workload, but labeling is subjective and teacher-dependent. Prior machine learning (ML) and deep learning (DL) approaches reported strong within-dataset results, yet were rarely evaluated in cross-dataset settings, leaving real-world generalizability unclear; meanwhile, LLM effectiveness for Bloom question classification has not been systematically studied. We evaluated the cross-dataset generalization of existing ML/DL methods and assessed LLMs with multiple prompting strategies on five datasets; the best prompting strategy combined in-context examples with course-specific action verbs. Supervised ML/DL models degraded substantially on unseen datasets, whereas LLMs were more stable, suggesting a robust alternative across diverse educational contexts. Based on the best prompting strategy, we also presented a lightweight UI that supports instructors in automatically classifying large question banks; a usability study indicated low workload and high usability.
Observing Teachers' Instrumental Pedagogical Orchestration in Synchronous Online Learning: A Multimodal Grid Based on Videoconferencing Traces
arXiv:2606.14358v1 Announce Type: new Abstract: Synchronous online teaching environments pose specific challenges for the analysis of pedagogical activity as teaching takes place via videoconferencing platforms and interactions are multimodal. While pedagogical orchestration has been extensively studied in the context of face-to-face courses and at the level of instructional design, the analysis of teaching in videoconferencing environments remains under-explored and insufficiently instrumented in terms of methodology. This article proposes a multimodal observation grid designed to analyze the instrumentalised pedagogical orchestration of teachers during synchronous online classes. Based on theories of pedagogical orchestration, multimodality and professional gestures of teachers, this grid identifies a set of observable indicators related to communicational gestures, posture, gaze and the management of digital tools. These indicators are structured and ranked in order of priority according to their observability and analytical relevance. They are operationalised to consider the constraints associated with data that can be analysed in videoconference class contexts. The proposed grid aims to provide a reproducible methodological framework for the analysis of instrumental pedagogical orchestration, with a view to future empirical validation.
Incentives Of EdTech: A Systematic Review Of EduNLP Research
arXiv:2606.13691v1 Announce Type: new Abstract: While the Natural Language Processing community has dedicated significant resources in developing educational technologies (EdTech) that support this shift, it remains unclear whose interests are being best served among the stakeholders of education. In this paper, we present a systematic literature review of 204 papers published in venues of the Association for Computational Linguistics' Special Interest Group on Building Educational Applications in 2024 and 2025, and validate these against EdTech papers from the wider ACL Anthology. By examining stakeholder inclusion and the prioritisation of research tasks, our findings reveal a critical tension: a push and pull between private-sector incentives and the foundational needs of educational infrastructure. Our analysis reveals that teachers are systematically under-represented as beneficiaries of research (33.3%) despite being the most affected, that real-world deployment remains rare (9.8%), and that ethical engagement tends toward acknowledgement rather than action. Drawing on exemplary papers in our corpus, we offer concrete recommendations for more responsible EduNLP research practices.
AI and changes in higher education
The rapid development of artificial intelligence (AI) is having a profound impact on the labour market, higher education, and students’ choices of academic disciplines and careers.
Microsoft president says AI backlash at graduation events should be wake-up call
Microsoft's president suggests that recent protests and backlash against AI at university graduation ceremonies serve as a necessary wake-up call for the tech industry.
Why Soft Skills Are Becoming the Most Valuable Career Asset in the Age of AI - Linkdood Technologies
For decades, career advice focused heavily on technical expertise. Learn to code. Master spreadsheets. Understand software. Develop specialized knowledge.
(Human) Attention Is (Still) All You Need: Human oversight makes AI-assisted social science reliable
arXiv:2606.12848v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for tasks once reserved for trained researchers, including hypothesis generation, specification choice, and drafting conclusions. We argue that the reliability of AI-assisted research depends not only on model capability, but also on how cognitive labour is structured between humans and machines.
An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration
arXiv:2606.12425v1 Announce Type: new Abstract: Active learning is widely recognized as an effective approach for improving learning outcomes in introductory programming courses. However, insufficient instructional support often limits students' access to timely, personalized feedback, which is crucial for mastering foundational programming concepts. Although recent advances in AI, particularly large language models, offer scalable opportunities for feedback, concerns about explainability and reliability remain. In this paper, we present an AI-driven classroom assistant that leverages an explainable AI model to analyze student code, map logical errors to instructor-identified misconceptions, and deliver instructor-authored feedback, thereby grounding reliability in instructor-defined pedagogical knowledge. To evaluate the effectiveness of our framework, we conducted an expert evaluation to examine its alignment with instructor-verified feedback and deployed the system in a classroom setting to assess students' perceptions of its usability. Results indicate that the assistant can provide accurate, instructor-verified feedback to students while fostering a positive experience.
AI SciBrief as a Gateway to Research: A Framework for Onboarding Students into New Research Areas
arXiv:2606.12413v1 Announce Type: new Abstract: Students at all levels of higher education face a significant barrier in the form of information overload, which often paralyzes the initial stages of the research process and suppresses motivation. In response, this article introduces a pedagogical framework that leverages AI SciBrief, a platform powered by a Large Language Model (LLM) designed to automatically generate digests of scientific trends. We describe how this multidisciplinary tool - with initial coverage in finance, medicine, and education - can be integrated into the curriculum to overcome this "entry barrier." The framework provides concrete methodologies for utilizing these digests to facilitate topic selection for term papers, accelerate literature reviews for dissertations, and enable postgraduate students to continuously monitor emerging trends. We conclude that AI SciBrief functions as a "gateway to research" effectively reducing students' cognitive load and empowering them to transition more rapidly from information searching to knowledge creation.
AI-Automation Tooling in Computer Engineering Education: Mixed-Methods TAM/UTAUT Evidence for a General Acceptance Attitude
arXiv:2606.12424v1 Announce Type: new Abstract: As generative AI and low-code workflow platforms become routine in software practice, a key educational question is whether the next generation of computer engineers will accept these tools as useful, usable, and worthy of sustained engagement. This paper reports a mixed-methods, cross-sectional study of undergraduate computer engineering students' acceptance of AI automation tooling, instantiated through the open-source platform n8n across three identically scripted workshops in Thailand (n = 103). A 12-item, five-point Likert instrument mapped to six TAM/UTAUT constructs - Performance Expectancy (PE), Effort Expectancy (EE), Behavioral Intention (BI), Self-Efficacy (SE), Hedonic Motivation (HM), and Output Quality (OQ) - was complemented by inductive thematic analysis of open-ended feedback. Analyses combined ordinal reliability estimation, bootstrap confidence intervals, non-parametric tests, multiple-comparison-controlled correlations, polychoric dimensionality diagnostics, a common-method-bias check, and between-session comparisons. Acceptance was favorable across all six constructs with large effect sizes, with PE emerging as the strongest construct and HM as the weakest. Dimensionality diagnostics further revealed that canonical TAM/UTAUT sub-facets collapsed into a single general acceptance factor in this short-form post-workshop context, a finding with important methodological and theoretical implications. Qualitative themes converged with the quantitative profile regarding usefulness and enthusiasm but diverged on output quality, revealing a small yet articulate reliability-skeptical minority. The findings support the curricular adoption of AI automation tooling in undergraduate computing education and identify three theory-grounded instructional levers: instruction-sequencing scaffolds, self-efficacy supports, and trust-calibration interventions.
Planning on Paper: Problem Decomposition with Diagrams in Introductory Computing
arXiv:2606.12427v1 Announce Type: new Abstract: Background and Context. Problem decomposition is a core concern of computing education. It has also become increasingly relevant: in response to GenAI, many CS1 educators are advocating for shifting instructional emphasis away from code writing and towards decomposition and higher-level planning. Currently, there is a lack of knowledge in how novices do decomposition in large, multifunction tasks. Objectives. In this study, we describe how students represent solutions to a decomposition task, and characterize common issues that arise in those representations. Method. In a 50-minute lab, students were given a description of a word game and asked to draw (with pencil and paper) a decomposition diagram for a program that would implement this game. We performed an inductive thematic analysis with negotiated agreement on 55 of the diagrams, coding salient elements (e.g. functions and the relationships between them) and issues that arose. Findings. Students used multiple representational strategies, including hierarchical function calls and sequencing (order of execution). We identified issues in notation (including use of differing, incompatible notations within the same diagram), order of execution, abstraction and reuse, encapsulation, clarity, and problem-specific misunderstandings. Implications. These findings suggest that novice decomposition is shaped by multiple underlying models of program behavior, with tensions between structural and sequence-focused reasoning. We discuss implications for decomposition instruction and future work, including clarifying representational constraints and plan tracing as simulation.
Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering
arXiv:2606.12422v1 Announce Type: new Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices. While automated scoring systems and machine learning techniques have existed for decades, generative AI (GenAI) now enables educators to implement standards-based grading (SBG) with unprecedented efficiency and scale. This paper examines the theoretical foundations and evaluates an LLM grader that uses commercially available foundation models with context and prompt engineering to score student work against a rubric. Drawing on an empirical interrater agreement study using Massachusetts Comprehensive Assessment System (MCAS) data, we observed the Quadratic Weighted Kappa (QWK) and Proportional Reduction in Mean-Squared Error (PRMSE) across mathematics, science, and ELA, using Claude Sonnet 4, Haiku 4.5, GPT-5, and GPT-5 Mini. The results demonstrate that LLM graders, especially when based on foundational models with more parameters, achieve substantial agreement with human raters in mathematics and science assessments, while the performances vary in ELA, suggesting generic foundation models can be effective at scoring in given contexts. Additional analysis of teacher and student feedback reveals strong acceptance of AI-generated narrative feedback but skepticism toward numerical scores, suggesting that LLMs function most effectively as formative tools rather than summative evaluators. Our findings indicate that thoughtfully designed hybrid models that combine AI efficiency with teacher judgment can reduce workload, enhance feedback quality, and support equitable assessment practices without displacing professional expertise.
GeoDial: A Multimodal Conversational Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns
arXiv:2606.12419v1 Announce Type: new Abstract: Several educational domains rely heavily on diagrams and visual cues, yet most existing tutoring datasets are limited to text-only interactions. This limits the development of AI tutors that can teach in visually grounded ways used by human instructors. Thus, we introduce GeoDial, a multimodal tutoring dataset of over 1.3K teacher-student dialogs in the domain of geometry collected from experienced math teachers, where instructional turns are explicitly grounded in diagram highlights. We propose a scalable annotation protocol that integrates dialog acts, visual highlighting, and feedback, enabling fine-grained supervision of both language and visual tutoring behavior. To illustrate the challenges posed by this setting, we fine-tune several vision-language models on GeoDial and evaluate their ability to generate tutoring utterances and diagram highlights. While supervised fine-tuning substantially improves the quality of generated dialog, it struggles to produce accurate diagram highlights, revealing a key limitation of current methods and highlighting the need for approaches that more effectively integrate visual reasoning with pedagogical interaction.
Who Designs the Designer? Behavioural Architecture for GenAI in Education
arXiv:2606.12416v1 Announce Type: new Abstract: AI in education is stuck between two failed responses: banning AI and building content-only tutors. Both fail because they ignore what decades of research has established: that personality, motivation, and emotional state shape learning outcomes as strongly as cognitive ability. This paper proposes behavioural architecture as an alternative. In the proposed architecture, the system adapts to how a student learns, not only to what they learn next. The student co-authors the record the system keeps, can read it, revise it, and revoke it. The designer role, what the system treats as true about the student, shifts from the AI vendor alone to a distribution among educator, student, and system. The paper argues that this architecture requires governance at EU level: the institution operating the system is the same one assessing the student, and individual institutions cannot provide the structural protections this configuration demands. Five empirical questions are proposed to test whether the architecture delivers on its claims. The contribution is naming a vacancy: the designer role in AI-in-education is currently unoccupied, and occupying it requires infrastructure that does not yet exist.
AI Specialization vs. Broad AI Literacy: The Academic Paradox
AI literacy and domain expertise are essential for augmented productivity, blending technical AI skills with critical human capabilities in a shifting job market.
The Environmental Cost of LLMs in AIED: Reporting and Practices
arXiv:2606.11215v1 Announce Type: new Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community. While LLMs offer unique avenues for learners and educators, using LLMs comes with computational and environmental costs. These costs are mostly hidden due to a lack of standardised procedures to measure and report these impacts. To address this gap, we first conducted a literature review of all papers published as part of the AIED 2025 conference proceedings, determining if and how computational or environmental costs of LLMs are reported. Most projects use LLMs, but few report computational resources used and almost none discuss environmental impacts of LLMs as an ethical concern. To address this lack of standardised reporting practices, we propose an open-source method for systematically measuring and reporting the computational expense of LLMs and environmental impact of running Machine Learning (ML) AIED systems. We provide software solutions to measure the carbon footprint for both local and cloud based hardware. We also provide an easy-to-use formula to calculate the computational expense of frontier LLMs even when the exact number of parameters is not known. Overall, we hope to motivate colleagues to use our method to strive for more transparent reporting of hidden costs of using LLMs in the AIED community.
Cambridge University launches £36m Zenith supercomputer
Zenith, a new AI supercomputer for science, has been launched at Cambridge University. Hosted at the university’s Ray Dolby Centre, the machine has been built by Dell and AMD. Its precise specs have not been revealed, but when funding for Zenith was announced in January, the university said it would provide a sixfold boost to […]
Digital skills shortage is the real AI bottleneck: OECD - Outsource Accelerator
The OECD's June 2026 Economic Outlook found that AI is not displacing workers at the scale feared — but a shortage of people with digital skills.
The skills people still perform better than AI, according to workplace experts - ABC News
Many workers fear machines will supplant them as adoption of artificial intelligence accelerates
RealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning
arXiv:2606.10254v1 Announce Type: new Abstract: While Large Language Models (LLMs) have achieved near-perfect performance in \emph{solving} high-school mathematics, their ability to \emph{evaluate} the diverse reasoning processes of real human students remains under-examined. To bridge this gap, we introduce \textbf{RealMath-Eval}, a rigorously annotated benchmark of 224 real-world exam responses from high schools. Our initial evaluation reveals that even state-of-the-art LLM judges struggle significantly on this task, exhibiting a high Mean Squared Error ($\sim$2.96) against expert human grading. To probe a plausible explanation, we contrast this performance with a control setting where the same judges evaluate synthetic LLM-generated solutions. We identify a stark ``Evaluation Gap'': judges are considerably more accurate and consistent on synthetic text (MSE $\sim$1.17) but struggle to generalize to authentic student reasoning. Through semantic embedding analysis, we find that synthetic errors suffer from a ``structural collapse'' into predictable, low-dimensional linear subspaces, whereas human errors form a more diverse error space. Furthermore, generative probability probes suggest that human reasoning involves significantly higher information-theoretic surprisal, indicating that student reasoning transitions are more out-of-distribution for current models. Finally, we find that surface-level style transfer fails to close this gap. Our findings suggest that current LLM evaluation pipelines relying heavily on synthetic data may not adequately capture the diversity of authentic student mathematical reasoning.
Establishing AI Procurement Guardrails for Student Safety
Opaque and insufficiently tested tools are increasingly shaping student outcomes without consistent transparency, civil rights review, or technical safeguards. States and the U.S. Department of Education can address these risks using procurement and oversight tools already within their authority.
The next digital divide may not be internet access, but AI fluency
Throughout the summit, speakers ... this requires multilevel collaboration among schools, industry groups, and government agencies to move in the same direction. Michelle Alarcon pointed out, though, that one bottleneck lies in the absence of a shared language around AI competency. She highlighted the Philippine Skills Framework (PSF), which is a government inter-agency initiative formed in 2021 designed to bridge the skills gap, align educational standards, ...
Survey finds only 19% of employees feel confident using AI tools at work | Benefits Canada.com
Only 19 per cent of employees feel confident using artificial intelligence at work, according to a new report by the Achievers Workforce Institute. The report, which analyzed responses from 3,000 global employees, revealed two connected trends: a two-year decline in the percentage of employees ...
Reshaping Undergraduate Computer Science Education in the Generative AI Era
arXiv:2606.07545v1 Announce Type: new Abstract: Generative AI represents a turning point for Computer Science (CS) education. In recent decades, post-secondary CS education has largely focused on what has been seen as practical software engineering skills: implementation-level programming, debugging, testing, and software design, analysis, and documentation. However, this framing is becoming less tenable as generative AI automates many of these tasks, challenging their centrality in CS education. To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts. This white paper outlines the findings of two international NUS-Google Workshops in Singapore, where we convened faculty members, industry practitioners, and students, and proposes a strategic response to reshape how CS should be taught at the undergraduate level. Based on the findings, we identify critical skills that must be preserved and those that are becoming less important. By incorporating these skills as "breadcrumbs," we can provide helpful nudges and engaging exercises within the current curriculum, enhancing learning experiences for everyone. We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI. It is important to consider a shift in curricula. Emphasizing system design, abstraction, and critical evaluation could greatly enhance their education and readiness for the challenges ahead. We propose prerequisites for solutions to reform CS education by fostering AI-native competencies, re-centering fundamental education, enhancing advanced pathways, embracing new pedagogies, and shifting institutional support.
In the Hybrid A.I.-Human Work Force, Who Will Actually Thrive?
A panel of experts explains how job seekers should prepare for the future of work.
AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond
arXiv:2606.07544v1 Announce Type: new Abstract: Middle school is a key window for building core academic skills and the learning routines students carry into later grades, yet many students still fall behind because help is often limited and comes too late, after they have already been stuck for a while. Learning Management Systems (LMSs) are now standard infrastructure for distributing materials, collecting work, assessing students' tasks, and recording grades, but in most deployments they still behave more like workflow tools than instructional supports. The result is the usual bottleneck: students keep practicing through confusion, teachers triage questions, and feedback that could have corrected the misunderstanding arrives after the misconception has already hardened. To address this gap, we propose an AI-integrated LMS for middle school instruction, paired with a longitudinal study design to test whether sustained, bounded AI support changes outcomes through high school and into post-high school pathways. The proposed platform adds policy-gated AI assistance to everyday coursework, delivering formative feedback and hinting, recommending spaced review and adaptive practice based on mastery, and providing teacher-facing dashboards that summarize misconception patterns and flag sustained struggle. Because the platform is intended for minors, the design is privacy-first, using data minimization, role-based access control, age-appropriate response constraints, and auditable logs of AI interactions. Beyond short-term performance, the evaluation plan links fine-grained learning traces (attempts, revisions, help-seeking, and pacing) to institutional outcomes where feasible, so we can separate tool adoption effects from longer-run changes in learning trajectories.
AI Growth Without Workforce Equity Will Deepen Global Divides
AI productivity gains may widen economic inequality unless nations expand access to skills, literacy, and opportunity.
Beyond Tool Adoption: A Practical Five-Stage Developmental Continuum for AI Literacy in Higher Education
arXiv:2606.00038v4 Announce Type: replace Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates. Yet students' engagement with AI tools often clusters at two extremes: avoidance driven by fear, mistrust, ethical concern, or lack of access, and uncritical reliance that produces fluent output while masking misunderstanding. Existing AI literacy frameworks provide valuable competency definitions, but most offer limited guidance for diagnosing where learners begin and how they progress toward responsible, critical engagement. This paper proposes a five-stage AI Literacy Continuum: 0) Not Yet Engaged, 1) Uncritical Use, 2) Informed Use, 3) Critical Evaluation, and 4) Improvement --that describes developmental orientations toward AI use in higher education. The continuum complements dimensional frameworks by providing educators with a practical diagnostic and instructional pathway aligned with international frameworks, including UNESCO and OECD. We present a design-based implementation case from North Carolina State University, where credit-bearing courses and intensive hands-on workshops engaged more than 330 participants between Fall 2024 and Spring 2026. Because the implementation did not use a validated pre/post instrument or comparison group, we frame the findings as observational and practice-based: participants exhibited behaviors consistent with movement from non-engagement or uncritical use toward informed engagement, while sustained and discipline-embedded experiences produced stronger evidence of critical evaluation and improvement-oriented practice. We discuss curricular pathways, opportunity considerations, assessment strategies, and argue that AI literacy should be understood not as tool adoption alone but as a developmental capacity to understand, evaluate, and responsibly apply AI systems in disciplinary and societal contexts.
AI is hiding America's literacy crisis
Millions of working Americans struggle to read at a functional level, and AI tools may be masking these skill gaps by allowing workers to complete tasks they do not fully understand.
IBM launches new AI learning pathway to upskill workforces at scale
/PRNewswire/ -- IBM (NYSE: IBM) today announced the expansion of IBM SkillsBuild, its free global technology education program, with a new artificial...
UK exam watchdog frets over smart specs turning GCSEs into Google searches
Ofqual warns that smart glasses and AI tools are creating new challenges for maintaining exam integrity.
Detective scaffolding for within-session reasoning development: a three-phase framework evaluated in polymer engineering and pre-university outreach
arXiv:2606.07279v1 Announce Type: cross Abstract: This paper presents a detective scaffolding framework -- a three-phase instructional sequence (Hypothesis Activation -> Evidence Structuring -> Causal Integration) in which engineering students investigate a realistic industrial defect scenario using staged in-class polls as designed evidence probes. Unlike conventional uses of student response systems for engagement, the framework positions each poll as an Evidence-Centred Design instrument targeting a specific reasoning capability. In the primary implementation, 80 Year~3 polymer engineering students progressed from prior-knowledge-driven misconception (71% attributing defects to temperature) to complete root-cause convergence (100\% identifying humidity; Fisher's exact test, $p < .001$) across four sequenced prompts within a single 90-minute lecture slot. A dual-accuracy analysis revealed that at one intermediate stage, textbook-correct and analytically valid responses diverged, illustrating why conventional scoring can misrepresent reasoning quality. In a transferability study, 26 Year~12 students with no engineering background achieved identical root-cause identification rates across two adapted scenarios, with significant gains in data-analysis confidence and AI explanation ability. The results suggest that the pedagogical structure, rather than disciplinary content, drives the convergence effect, implying portability across disciplines and educational levels.
How global AI forums are redefining university credibility - The Economic Times
As AI reshapes higher education, universities are increasingly being evaluated on their readiness for an AI-driven future. Beyond traditional measures of excellence, engagement with global AI ecosystems and conversations is emerging as a key signal of relevance.
Growing number of AI hallucinations that are appearing in academic papers
Concerns are rising as AI-generated hallucinations increasingly infiltrate academic research and publications.
CrowdMath: A Dataset of Crowdsourced Mathematical Research Discussions
arXiv:2606.06526v1 Announce Type: new Abstract: Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step solutions, or complete proofs. They do not capture collaborative open-problem solving: a setting in which participants propose partial arguments, identify gaps or errors in prior steps, repair flawed reasoning, and gradually synthesize incremental contributions into a proof. We introduce CrowdMath, a dataset of 164 expert-annotated progress chains from the MIT PRIMES--Art of Problem Solving (AoPS) CrowdMath program (2016-2025), a collaborative research initiative whose discussions have led to peer-reviewed publications. Each chain traces a multi-participant forum discussion from an open-problem statement to a completed proof. Posts are labeled by their functional roles in the evolving solution process, including partial progress, proof completion, erroneous reasoning, and error identification. We define evaluation tasks and benchmark six frontier models. Models achieve 83-88% accuracy on next-post prediction, suggesting that they can follow the local flow of mathematical discussion. However, they struggle to identify the functional significance of individual contributions with the best model achieving only 0.42 macro-F1 on post-role classification. CrowdMath exposes a gap between solving well-specified mathematical problems and understanding collaborative mathematical progress as it unfolds.
mmPISA-bench: Do LLMs Reason Equally Well Across 43 Languages?
arXiv:2606.07069v1 Announce Type: cross Abstract: We introduce mmPISA-bench, a compact high-quality multilingual reasoning benchmark derived from the OECD Programme for International Student Assessment (PISA). The benchmark consists of 25 multiple-choice questions that require reasoning in order to be answered correctly. Each question is provided in official human translations to 43 languages and complemented with machine-translated counterparts (i.e., 2,150 data points in total). We evaluate two mainstream proprietary LLMs across languages, reasoning effort levels, and translation types in terms of their ability to answer the questions correctly. Our results show that modern LLMs can reason effectively across all evaluated languages, achieve accuracy comparable to human test-takers, with some performance variations across covered languages. We further find that machine-translated questions do not degrade accuracy relative to official human translations which suggests that high-quality machine translation (synthetic data) might often be adequate for large-scale multilingual reasoning evaluations where official translations are not available. Finally, we analyze token usage and related inference cost and find that LLMs usage in some languages is simultaneously more expensive and less accurate.
AI is eliminating entry-level jobs. Education needs to fill the gap - Fast Company
As employers reduce entry-level hiring, colleges and universities must rethink how students gain the skills and experience once built through jobs.
LeanMarathon: Toward Reliable AI Co-Mathematicians through Long-Horizon Lean Autoformalization
arXiv:2606.05400v1 Announce Type: new Abstract: Long-horizon autoformalization of research mathematics fails not only at hard lemmas, but at scale: statements drift, dependencies tangle, context decays, and local repairs corrupt distant work. We present LeanMarathon, a multi-agent harness for reliable research-level Lean autoformalization. Its core abstraction is an evolving blueprint: a Lean file that serves simultaneously as formal proof skeleton, natural-language proof graph, and shared system of record. Four contract-scoped agents construct, audit, prove, and repair this blueprint. These agents are coordinated by a two-stage orchestrator that first stabilizes target fidelity through adversarial review and then discharges the proof directed acyclic graph (DAG) from its dynamic leaves upward in parallel CI-gated rounds. LeanMarathon turns one brittle multi-hour run into many local, recoverable, parallel transactions. We evaluate LeanMarathon on two recent research papers spanning four Erd\H{o}s problems (#1051, #1196, #164, #1217). Across three autonomous runs, it formalizes all seven target theorems with no sorry, proving 258 lemmas and theorems. These results show that reliable AI co-mathematics requires not only stronger provers, but durable harnesses that preserve target fidelity across long mathematical developments. The code can be found at https://github.com/YuanheZ/LeanMarathon.
KUOW - Most K-12 teachers say AI's impact on education will eclipse the internet or computers
A new NPR/Ipsos poll shows many teachers are using AI to save time, but a majority are also worried the technology is making it harder for students to learn to think for themselves.
Preparing students for an AI-powered future: Why curricula must evolve
Issues such as misinformation, deepfakes, data privacy, cybersecurity, and algorithmic bias are increasingly relevant to students’ daily experiences. AI-generated content can often appear convincing, even when it contains inaccuracies. As a result, students need to develop the ability to verify information, assess sources, and distinguish between reliable and misleading content. Embedding discussions on digital ethics, responsible technology use, and media ...
Agentic AI and Pedagogical Best Practice: The Tension Between Automation and Learning
arXiv:2606.04543v1 Announce Type: new Abstract: Artificial intelligence in education is evolving from passive chatbots to proactive AI agents capable of initiation and goal-directed interactions. While offering opportunities for personalised learning, this shift risks undermining learner agency and cognitive effort. This paper reviews six pedagogical principles-prior knowledge activation, collaborative learning, problem-based learning, formative assessment, scaffolding, and metacognition-through the lens of agentic AI. We discuss the tension between automation and learning, proposing design recommendations that prioritise intentional friction, dynamic scaffolding, human-in-the-loop oversight, and considered AI utilisation to ensure AI supports rather than supplants human learning.
Colleges and Institutes Canada - Skills Central to Canada's AI Opportunity
At a pivotal moment for Canada's future, Colleges and Institutes Canada (CICan) welcomes the federal government's new National Strategy on Artificial Intelligence: AI for All, a bold and timel. . .
For students, generative AI raises a new question: Will more education still pay off?
For a century, technology raised the value of schooling. Generative AI may be different, reaching into white-collar work and clouding students’ economic future.
SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice
arXiv:2606.04155v1 Announce Type: cross Abstract: Social skills such as negotiation and leadership are crucial for personal and professional success in today's interconnected world. However, scalable and effective training remains a significant challenge due to the scarcity of expert coaching. In this paper, we introduce SocialCoach, a holistic LLM-powered agentic tutoring system for personalized social skill development at scale. First, SocialCoach automatically constructs a pedagogically-grounded, theory-to-practice knowledge corpus from diverse expert sources, leveraging a multi-agent pipeline. Second, to personalize the learning journey, it employs an adaptive practice scheduling module that follows a prescription-retrieval-adaptation process. To maximize the long-term learning experience while overcoming the cold-start problem, this policy is optimized within a learner simulation environment through reinforcement learning. Finally, SocialCoach integrates immersive, goal-driven practice, causality-driven proficiency assessment and knowledge-grounded, reflective tutoring to help address the knowing-doing gap. We deploy it in our product, EQoach, and conduct extensive experiments. The results show that SocialCoach improves simulated pathway quality and judge-rated tutoring quality over baseline approaches, while early user feedback indicates strong perceived engagement and usefulness. These findings suggest a practical architecture for personalized and gamified pedagogical platforms on soft skill learning.
Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
arXiv:2606.04152v1 Announce Type: new Abstract: Large language models are reshaping research practice while quietly eroding researchers epistemic accountability. This commentary introduces PEEL - Protocols for Epistemically Engaged Literacy in AI, a working scaffolding that combines deterministic distant reading via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics and abductive reasoning. Applied to AI-generated condensations of three source texts, PEEL reveals systematic distortions in quantity, term frequency, and epistemic voice that are invisible without non-AI measurement -- and yields three design implications: deterministic instruments must accompany AI tools; fluency is not fidelity; epistemic authority must be designed in, not assumed.
Behavioral and Performance Indicators of Depression and Anxiety in Electronic Learning Systems
arXiv:2606.04254v1 Announce Type: cross Abstract: This study investigates whether behavioral and performance indicators derived from a Moodle-based learning management system are associated with university students' depression and anxiety in two undergraduate Computer Engineering courses. Using a quantitative observational design, LMS event logs, academic records, and self-reported Beck Depression Inventory-II and Beck Anxiety Inventory scores from 97 students were integrated. A broad set of behavioral and performance indicators spanning temporal engagement, session structure, deadline-related behavior, page-refresh patterns, and LMS navigation was extracted from raw event logs and analyzed using descriptive statistics, independent-samples t-tests with Benjamini-Hochberg FDR correction, effect sizes, and Spearman correlations; inventory scores were confirmed invariant by sex and academic year. Several indicators were significantly associated with depression and anxiety. Higher depression was associated with shifted temporal activity patterns, longer session durations, and shorter homework submission lead times, while higher anxiety was associated with concentrated temporal engagement and session-based differences. These findings suggest that routine LMS data can provide meaningful behavioral signals related to student well-being and may support earlier educational awareness of students who experience mental-health-related strain. At the same time, such indicators should be interpreted as contextual and non-diagnostic markers rather than as substitutes for clinical assessment.
Does Artificial Intelligence Advance Science?
arXiv:2606.05118v1 Announce Type: new Abstract: This paper examines whether and how artificial intelligence (AI) advances scientific creativity. Drawing on scientific publications, the primary output of researchers, we analyze over one million publications from OpenAlex to investigate the relationship between AI adoption and multiple dimensions of scientific creativity, including novelty (recombi
Characterizing initial human-AI proof formalization workflows
arXiv:2606.04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been a challenge. Advances in AI systems' ability to generate code and engage in increasingly high-level mathematical reasoning promise to transform people's ability to formalize and thereby verify proofs. While many works focus on benchmarking the current frontier, we instead study how people use these tools. We conduct a mixed-methods analysis into the initial impact of AI on people's formalization workflows: what people claim they want, what they see as the barriers to those visions, and how they actually use and adapt AI in practice. A qualitative survey shows that people's preferences are diverse, but with a general desire for AI assistance in formalization that preserves high-level human control over the proof discovery process. To assess how people actually engage with AI for formalization under such limitations, we conduct a controlled user study in which participants formalize informal math problems and their proofs, with and without AI, across a range of mathematical problems at varying levels of difficulty and domains. Despite limitations of the tools at the time for autoformalization, participants tend to attain higher formalization accuracy when allowed access to AI tools than when formalizing on their own, with most participants flexibly choosing to use multiple different AI tools. Taken together, our work sheds light on the early stages of AI integration into formalization workflows, involving an intimate interplay of human and AI engagement.
Smartglasses and earpieces may worsen exam cheating in schools, says Ofqual
Stronger checks likely to be needed in England to safeguard reputation of GCSEs and A-levels, says Ian Bauckham Cheating in exams could be magnified by the new generation of wearable hi-tech devices such as smartglasses or invisible earpieces, according to England’s qualifications watchdog. Ian Bauckham, the head of the Office of Qualifications and Examinations Regulation (Ofqual), also revealed that GCSEs and A-level courses in England were being scrutinised over potential AI use in students’ coursework, after teachers said they were struggling to detect it. Continue reading...
VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
arXiv:2606.04244v1 Announce Type: new Abstract: Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then reason over the tool's output, specifically when they rely on visual aids. This gap is especially important because real engineering and scientific workflows often rely on visualization tools for analysis, validation, and decision-making. To study this discrepancy, we introduce VAMPS (Visual-Assisted Mathematical Problem Solving), a benchmark for graph-assisted mathematics. VAMPS contains 1,168 multimodal, bilingual multiple-choice question-answer pairs drawn from Iranian University Entrance Exam algebra and calculus problems and expanded with human-reviewed LLM-generated synthetic variants, all selected so that plotting provides a natural solution strategy by revealing intersections, extrema, asymptotes, etc. Designed for both benchmarking and diagnosis, VAMPS goes beyond prior multimodal benchmarks that primarily evaluate reasoning over fixed visual inputs by testing whether a model can benefit from constructing a useful graph and grounding its answer in the resulting visualization. Overall, we found that across a diverse set of models, direct analytical solving surprisingly outperforms tool-enabled visual solving, even on problems where plotting is a natural strategy.
NSF renews support for MIT-led AI and physics institute, expanding a new model for discovery
The National Science Foundation has renewed its funding for the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, led by MIT.
PATH to boost AI training and career opportunities for industry-aligned jobs
MIT and Georgia State University have announced a new partnership called PATH to create industry-aligned AI training and career pathways.
Failing grades soar with AI use: Sridhar Vembu says AI can make you smart or dumb - India Today
Professors at the University of California, Berkeley say rising AI use is coinciding with more failing grades in computer science courses. The debate now centres on how students can use the technology without weakening core thinking and problem-solving skills.
An addictive drug that must be researched, studied and confined - The Washington Post
The first time I assigned this project, I worried that I would get them addicted to Character. AI — arguably the most seductive and addictive type of chatbot for young people, because it gives students exactly what I assumed they wanted: a friend who never says no, never gets tired and never pushes back.
The IBM executive tasked with retraining 30 million workers is changing how she thinks about the AI finish line
Justina Nixon-Saintil has reached 22 million learners. With three years left on her mission, she says the real challenge is only now coming into sight.
AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study
arXiv:2606.03288v1 Announce Type: new Abstract: Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python, N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.
Sydney academic used AI to write SMH opinion piece urging students to avoid using tech to ‘cut corners’
Sydney Morning Herald removes piece by Cath Ellis, despite Western Sydney University saying her use of AI was ‘appropriate’ Follow our Australia news live blog for latest updates Get our breaking news email, free app or daily news podcast A top Sydney academic used AI to write an opinion piece that urged students to “do the work” and not cut corners by using such technology, with the Sydney Morning Herald removing the “unacceptable” piece from its website. Western Sydney University’s pro vice-chancellor for quality and integrity, Prof Cath Ellis, had an opinion piece published in the Sydney Morning Herald last month, in response to an article from the academic Kylie Moore-Gilbert. Continue reading...
Designing a Hardware Reverse Engineering Course: Lessons from Eight Years in a Rapidly Evolving Tech Domain
arXiv:2606.03697v1 Announce Type: new Abstract: Integrated Circuits (ICs) are omnipresent, yet their globalized manufacturing process remains vulnerable to supply chain threats. Hardware Reverse Engineering (HRE) is essential for detecting such threats and re-establishing trust; however domain experts remain scarce due to a lack of educational programs. To contribute educational insights in this critical and rapidly evolving technology domain, we present our HRE course focusing on digital circuit analysis and digital circuit extraction from ICs. The course targets junior-level undergraduates at a major European research university. The curriculum has been refined over nine iterations (2017-2025), with several alumni subsequently pursuing careers in the HRE field. By reflecting on the evolution of the course organization, content, and assignments, we derive key lessons learned. We further distill these insights into actionable design priorities for educators developing courses in rapidly evolving technological domains, emphasizing iterative growth and sustainable workload management for both students and instructors.
Merit or networks? What decides where research is published
arXiv:2606.03763v1 Announce Type: new Abstract: Does scientific publishing reward the quality of ideas or the advantage of connections? The question is universal to prestige-driven science, yet it has resisted decades of study because a paper's quality could not be gauged ahead of its publication fate without using that fate as the yardstick. We break this constraint by measuring a paper's idea q
Tracing GenAI Literacy: Uncovering Student-AI Interaction Patterns in Academic Writing through Epistemic Network Analysis
arXiv:2606.00040v1 Announce Type: new Abstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical. However, current assessments largely rely on self-reported scales, lacking insights into how literacy manifests in actual learning processes. This study leverages Learning Analytics (LA) to bridge this gap. We collected interaction logs from 162 university students engaged in a GenAI-assisted abstract writing task. Using Epistemic Network Analysis (ENA), we modeled and compared the questioning strategies of students with varying GenAI literacy levels. Preliminary results reveal distinct interaction signatures: high-literacy students engage in iterative refinement and strategic questioning, while low-literacy students rely on direct generation commands. This work contributes to the workshop by demonstrating how process data can characterize GenAI literacy, paving the way for data-driven literacy assessment and real-time interventions.
Artificial intelligence as a real game to enlighten science education for disabled students in rural New Mexico
arXiv:2606.00034v1 Announce Type: new Abstract: Artificial Intelligence AI has emerged as a transformative innovation in inclusive science education for disabled learners in rural New Mexico. Using a mixed method design that combined multiple linear regression and an Artificial Neural Network ANN model, this study examined 120 students in grades 6 to 10 and 15 instructors across four rural schools. The AI-based learning intervention predicted student performance with high accuracy R2 equals 0.92, and p less than 0.05. Experimental results showed a 32 percent improvement in science concept retention, a 27 percent increase in laboratory performance, and a 42 percent rise in student engagement following the intervention. These findings demonstrate that AI-driven pedagogy can serve as a transformative equalizer, improving engagement, comprehension, and accessibility for disabled learners. The study concludes that AI is a promising tool for achieving equitable science education in underserved rural settings.
The Fed says a labor-market phenomenon is hurting youth employment — and it's not AI
A popular narrative recently has been that AI is causing employers to rethink entry level jobs. The New York Fed says remote work is the bigger issue.
Remote work – not AI – is killing job prospects for the youth
Young professionals may be perfectly productive while working from home, says the New York Fed, but the quality of their output isn't so great, so companies don't want to hire them
AI Use Among Students Transforms Homework Ethics And Learning Approaches In Higher Education
Students openly admit using ChatGPT for assignments, brainstorming and revisions. As AI becomes impossible to ignore, educators are rethinking homework, cheating and what it means to actually learn.
Reinforcement Learning for Special Education: Aligning LLM Tutors to Diverse Learners through Disability-Adaptive Training
arXiv:2605.30670v1 Announce Type: new Abstract: Large language models are increasingly deployed as intelligent tutors, yet research on aligning them for special education remains absent. Recent work has applied reinforcement learning to LLM tutors, but these methods target a generic learner in a single domain (mathematics) and do not address the cognitive and communicative diversity of learners with disabilities. We introduce \emph{Special-R1}, a framework that extends pedagogical RL to special education through two components: (1) a two-dimensional adaptive system prompt that couples a difficulty-based support level with a disability-specific teaching style across five disability profiles; and (2) a persona-aware Thinking Reward whose judge rubric is conditioned on the learner's disability profile. On a persona-augmented test set of 690 multi-turn dialogues, our full model raises persona-aware Fit from 6.75 (generic baseline) to 8.40 (+1.65) and SPED-rubric Helpfulness from 0.720 to 0.768, leading on the four-component Total (2.911, +0.064 over the runner-up) while remaining within 0.01 of the strongest variant on the out-of-domain OpenLearnLM benchmark (8.53). Ablations show that the Thinking Reward becomes effective only in combination with adaptive prompting, and that residual weakness on specific learning disability in mathematics motivates targeted multimodal extensions.
How Much Will AI Impact Tomorrow’s Workforce? New Data on the Future of Work with AI | by MIT IDE | MIT Initiative on the Digital Economy | Jun, 2026 | Medium
Three papers from MIT IDE research scientists offer insights leaders can use to prepare for an AI–powered workforce.
Guess Who’s Got an AI Edge in a Tough Job Market? - Bloomberg
Mentioning artificial intelligence to the graduating class of 2026 has been sure to get you booed. And why not? Fresh graduates have spent the past few years being told about the wonders of AI and watched seniors struggle to get a toehold in the labor market.
The Tutoring Effectiveness Index: Predicting LLM Math Tutor Quality from Four Conversation Signals
arXiv:2605.30666v1 Announce Type: new Abstract: Aligning large language models (LLMs) as math tutors typically demands costly reinforcement-learning (RL) training and external LLM judges. We ask whether a frozen model's internal reasoning signals can replace both. We propose the Tutoring Effectiveness Index (TEI), a training-free, judge-free four-signal index that combines a Schoenfeld-Verify key
7 AI Certifications Worth More Than a Degree in 2026
A four-year degree often signals theoretical competence. The best AI certifications signal immediate capability.
Which jobs might be most at risk of being erased by AI in developing countries? | Human Resources Online
AI threatens to quickly automate clerical and administrative roles in low-income countries — some of the few better‑quality jobs and a vital pathway to decent work, especially for women and young people, the ILO warns.
Practitioner Beliefs and Behaviors in AI-Enhanced Education: DOT Framework Survey Evidence
arXiv:2605.29041v1 Announce Type: new Abstract: This study reports findings from a cross-sectional survey (n = 72) of higher education practitioners examining beliefs, behaviors, and institutional conditions related to artificial intelligence (AI) integration in teaching and learning. Grounded in the DOT Framework, which integrates design thinking and open systems theory, the study investigates AI familiarity, usage patterns, design-oriented practices, and pedagogical beliefs. Exploratory factor analysis of 19 belief items identified a three-factor structure: AI Functional Capabilities, Oversight and Governance, and Instructor Collaboration and Planning ({\alpha} = .90). Results indicate that practitioners hold favorable views of AI as a pedagogical support while maintaining strong commitments to human oversight and critical evaluation. Reported practices emphasize iterative prompting and content generation, with less consistent use of needs assessment and feedback loops. Institutional barriers including limited policy, training, and infrastructure were widely reported. These findings provide preliminary empirical support for the DOT Framework as a descriptive model of practitioner beliefs and practices, while also highlighting gaps between design-oriented theory and current implementation. The study contributes an initial measurement structure and identifies directions for confirmatory validation and outcome-based research linking AI-supported design practices to instructional quality.
Review Arcade: On the Human Alignment and Gameability of LLM Reviews
arXiv:2605.28897v1 Announce Type: new Abstract: LLM-generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM-assistance, but also that authors use LLMs to revise their papers before submitting. In this work, we perform empirical experiments on papers from the 2025 ACL Rolling Review (ARR) to evaluate LLM reviews from both the author and the reviewer perspective. First, we identify a limited alignment of LLM reviews with human ones. In the best-case scenario, the alignment is reasonable. However, we also find that LLM-human alignment varies substantially across prompts and models. Finally, we investigate the scenario in which the author uses an iterative draft-revise workflow to improve the submission according to the LLM review. We find that this "gaming" of LLM reviews can be effective in specific scenarios, leading to a statistically significant increase of overall scores for up to 35\% of papers. We publish our code: https://github.com/uhh-hcds/reviewarcade.
Generalizing a Highly Configurable Analytics Pipeline to Replicate and Support Educational Research Across Multiple Domains
arXiv:2605.30303v1 Announce Type: new Abstract: Artificial intelligence assistants deployed in online learning environments create new opportunities to collect large volumes of learner interaction data and generate insights to improve student outcomes. Architecture for AI-Augmented Learning (A4L) is a modular data architecture that enables the collection, integration, and analysis of learner interaction data from educational AI systems, supporting the generation of instructional insights that facilitate personalized learning and reinforce the bidirectional feedback loop between instructors and learners. This study examines the modular design of the A4L Data Analytics Pipeline, an extensible data infrastructure that enables the ingestion, processing, and analysis of heterogeneous datasets generated by educational AI assistants. We describe the design principles and development process used to extend the pipeline's analytical capabilities while preserving flexibility across domains. We evaluate the pipeline through case studies spanning three research domains corresponding to three educational AI assistants deployed in online learning environments at Georgia Tech. Results show that a common set of statistical analysis methods can be consistently applied across datasets with differing structures and instructional contexts, enabling the pipeline to reproduce key analytical findings across domains. We demonstrate how analytical capabilities initially developed for one domain can be extended to support richer analyses in another, illustrating the pipeline's extensibility. These findings suggest that the A4L Analytics Pipeline can serve as reusable infrastructure for analyzing data generated by future educational AI assistants. By enabling analytics that can be systematically extended to new domains, the pipeline provides a foundation for deriving insights that inform the design and evaluation of educational AI systems.
Aryabhata 2: Scaling Reinforcement Learning for Advanced STEM Reasoning
arXiv:2605.28829v1 Announce Type: cross Abstract: Competitive STEM examinations such as JEE and NEET require multi-step symbolic reasoning, precise numerical computation, and deep conceptual understanding across physics, chemistry, and mathematics. Recent large language models perform strongly on common reasoning benchmarks, yet they remain difficult to deploy at scale, where millions of student doubts demand domain-specific, consistently structured problem solving. We introduce Aryabhata 2, a reasoning-focused language model for competitive STEM examinations, trained via reinforcement-learning post-training. Using PhysicsWallah's internal question banks, we construct a high-quality training curriculum and post-train GPT-OSS-20B through reinforcement learning with verifiable rewards. Training combines prolonged reinforcement learning with broadened exploration via progressively larger rollout group sizes. We evaluate Aryabhata 2 on competitive examination benchmarks, including JEE Main, JEE Advanced, and NEET, as well as out-of-distribution reasoning datasets such as AIME, HMMT, MMLU-Pro, MMLU-Redux 2.0, and GPQA. Results show that Aryabhata 2 outperforms its base model GPT-OSS-20B on competitive STEM reasoning while requiring substantially fewer output tokens (up to 64\% fewer).
Smaller, Younger, and More Impactful: How AI-Assisted Writing Transforms Research Teams
arXiv:2605.27404v1 Announce Type: new Abstract: The era of Big Science has long been defined by increasingly large and specialized research teams pushing the frontiers of knowledge. However, recent advances in artificial intelligence (AI), particularly large language models (LLMs), are beginning to reshape academic writing and scientific research, potentially disrupting the longstanding trend tow
Mathematical Modelling of Ethical AI Use in Higher Education: A Coordination Game Framework for Future-Facing Learning
arXiv:2605.27400v1 Announce Type: new Abstract: The rapid uptake of generative artificial intelligence (AI) in higher education is reshaping assessment practices and intensifying concerns around academic integrity, fairness, and learning quality. While institutional responses increasingly emphasise policy guidance and ethical principles, there remains limited formal understanding of how collective norms of responsible or opportunistic AI use emerge and stabilise within student cohorts. This paper reframes student AI use in assessment as a coordination problem shaped by peer expectations and assessment design rather than individual compliance alone. We develop a coordination-based evolutionary game-theoretic framework that captures learning value, effort, perceived fairness, and transparency, with institutional AI governance modelled implicitly through reflective assessment incentives. We use analytical results and finite-population simulations to reveal threshold-driven behavioural transitions in student AI use: small, well-calibrated changes in reflective assessment incentives can trigger rapid shifts towards responsible, learning-oriented AI-use norms, whereas weak or misaligned incentives allow opportunistic practices to persist. These non-linear dynamics explain why policy statements alone often fail to change behaviour, while modest assessment redesigns can have disproportionate effects. By providing a mechanism-level account of how assessment structures shape collective AI-use practices, this work offers higher education institutions an analytically grounded tool for Future Facing Learning, supporting proportionate, pedagogy-led AI governance without reliance on surveillance or punitive enforcement.
Govt partners industry to revamp AI curriculum to bridge skill gap
The Government is working on a comprehensive overhaul of the Artificial Intelligence (AI) curriculum to align academic learning with emerging technological trends and industry requirements.
Soro: A Lightweight Foundation Model and Chatbot for Tajik
arXiv:2605.27379v1 Announce Type: new Abstract: We present Soro, a family of Tajik-specialized conversational large language models (LLMs) designed for real-world deployment under tight compute and connectivity constraints in Tajikistan. Starting from open-weight Gemma 3 checkpoints, we perform Tajik-only continual pretraining on a curated 1.9-billion-token corpus spanning filtered web text, PDF documents, and curriculum-aligned educational materials, followed by supervised instruction tuning on 40K Tajik teacher-style examples. To enable rigorous evaluation despite the limited coverage of Tajik in standard benchmarks, we introduce a suite of Tajik benchmarks covering general knowledge, linguistic competence, and school- and university entrance-exam domains, and we open-source them on Hugging Face. Across these Tajik benchmarks, Soro substantially outperforms same-size Gemma 3 baselines while retaining strong English performance on standard datasets. We further show that FP8 and INT4 quantization of Soro preserves most Tajik-language gains while reducing memory requirements for edge deployment, supporting an ongoing education-sector pilot and planned scale-out across schools in Tajikistan.
LLM-assisted sentiment analysis for integrated computational and qualitative mixed methods education research: A case study of students' written reflection assignments
arXiv:2605.27403v1 Announce Type: new Abstract: Written reflection assignments give students valuable opportunities for critical self-assessment, meaning making, and learning processing. Additionally, such reflections provide rich data for qualitative education research. However, qualitative data can be time-consuming to analyze. It is even more time-intensive to qualitatively compare findings between different groups of participants, usually limiting comparison to, at most, one variable (e.g., binary gender). Large language models (LLMs) have recently begun to be critically evaluated for use as qualitative research assistants. Using a longitudinal case of written student reflections (n=151) from a study abroad program, we investigate how LLM-assisted sentiment analysis can enable longitudinal mixed-methods research combining computational and thematic analyses. First, statistical testing is used to quantitatively compare sentiment differences according to seven different student identity/lived experience variables. Then, these results inform qualitative data analysis to investigate the reasons underlying these differences. For the case of undergraduate students studying abroad, we found that prior experience living abroad was the only personal variable impacting students' sentiments of their verbal language and communication behaviors. This workflow has implications for how qualitative researchers can more easily probe multiple variables when comparing participants from different demographic groups.
REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
arXiv:2605.27402v1 Announce Type: new Abstract: Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for automated grading systems. Although recent neural and large language model (LLM) based systems have demonstrated superior performance, they are typically black-box models whose scoring processes and rationales are difficult for educators to verify and trust. Concept bottleneck models (CBMs) have emerged as a promising approach by routing predictions through human-interpretable concepts, providing a mechanistic guarantee of transparency. However, standard CBMs are not tailored to open-ended grading: they do not explicitly model fine-grained rubric dimensions, inadequately capture the ordinal semantics of scoring scales, and neglect inherent reliability issues in human concept annotations. To address these limitations, we propose REC-CBM, a rubric-aware error-correction concept bottleneck model for trustworthy open-ended grading. REC-CBM introduces a rubric-aware concept encoder that learns concept-specific representations over responses and an ordinal pairwise calibration objective that preserves ranking structure among rubric dimensions. It further incorporates a latent concept error-correction module that denoises concept predictions before final grade prediction while preserving interpretability. Comprehensive experiments on publicly available datasets show that REC-CBM consistently improves grading performance and produces more faithful concept-level reasoning than both state-of-the-art baselines. Further analyses validate the contribution of each component and demonstrate the applicability in realistic educational settings. Overall, this work provides a practical, interpretable grading solution that enables educators to inspect, intervene in, and trust automated decisions, advancing more transparent and trustworthy education.
Learning after COVID-19 and the ICT career aspirations: Are students entering the AI era with weaker skills?
arXiv:2605.27391v1 Announce Type: new Abstract: This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship between learning environments and changes in ICT related career aspirations across countries. The analysis uses country-level data from PISA 2018 and 2022, combining indicators of student autonomy, digital skills and teacher support. A mixed-method approach is applied, including descriptive statistics, regression analysis, clustering, latent representation learning (using Variational Autoencoder-VAE), discriminant analysis and probabilistic modeling to capture both observable and latent dimensions of educational readiness. Unlike prior research that treats learning loss, digital skills and career expectations separately, our analysis integrates them within a comparative longitudinal framework. It shifts the focus from short-term post-pandemic effects to the structural capacity of education systems to prepare students for digital and AI-driven labor markets. Results show a global but uneven increase in ICT career aspirations. Digital skills emerge as the strongest and most consistent predictor, while teacher support plays a complementary role. Autonomy shows weaker, context-dependent effects. Educational readiness is multidimensional, and ICT aspirations evolve relatively independently from other career domains.
Is AI to blame for hiring woes faced by college graduates? - ABC11 Raleigh-Durham
Analysts disagree about whether AI is a factor in the hiring crunch.
The AI Hype Index: AI gets booed in graduation season
It is one thing to say AI will change the world. It is another to expect the class of 2026 to applaud it. In fact, when former Google CEO Eric Schmidt told University of Arizona graduates that their task is to help shape AI, he was met with a resounding chorus of boos. “I can…
Ozone Mbanefo pushes radical rethink of Nigerian education as AI reshapes global workforce – The Sun Nigeria
As Artificial Intelligence rapidly transforms industries across the globe, the founder of 02 Academy Nigeria, Ozone Mbanefo, has called for an urgent overhaul of Nigeria’s education system, warning that many institutions are still preparing students for jobs and realities that are fast ...
Governor Healey Announces $25 Million for New MIT Quantum Systems Lab, Strengthening MA’s Global Leadership in Quantum Computing Research | Mass.gov
State matching funds will keep Massachusetts, MIT and UMass Boston at the forefront of quantum computing research, create hundreds of new jobs, and provide opportunities for students
Teachers lack formal AI guidance for learning and instruction, Gallup finds | K-12 Dive
Teachers in higher-needs schools were less likely than those in wealthier schools to have received guidelines, echoing previous research.
Why Students Need Both Soft Skills and AI Skills for Future Careers
Workplaces of the future are changing rapidly. Thanks to AI and automation, industries all over the world will change how corporations staff, work and grow. Corporations have realized that AI-focused...
The AI literacy paradox: Why students feel unprepared for the AI-driven workforce - eCampus News
The AI literacy paradox: Why students feel unprepared for the AI-driven workforce - eCampus News Key points: - Now is the time to educate students in proper AI application - AI is reshaping entry-level work and the talent pipeline - Aligning AI with pedagogy, privacy, and outcomes - For more news on how students use AI, visit eCN’s AI in Education hub Colleges and universities nurture student development by offering thousands of learning opportunities, and for most, the experience makes it possible to pursue meaningful careers. Whether preparing for a career in business, finance, healthcare, science, technology, or even the arts, stepping into the workforce today requires knowledge of AI–its benefits, its blind spots, and the ethics with which it must be applied. But could schools be doing more to equip students with the knowledge they need to apply AI academically and employ it stra
IBM Highlights India's Potential in AI Workforce Re-skilling, ETEnterpriseai
IBM India head Sandip Patel emphasizes the importance of re-skilling India's young workforce to harness the country's potential as a global AI powerhouse, addressing the need for governmental and corporate support in AI training.
It’s time to address the looming crisis in entry-level work.
Artificial intelligence has not so far produced a clean story of mass unemployment. Aggregate employment in developed countries remains broadly stable, and recent assessments have found limited evidence that AI has shifted the headline numbers. But a troubling change may be hiding beneath the surface: the quiet weakening of the first rung of the career…
Inside The Government’s Big Bet On Coordinating AI Workforce Readiness
A new federal initiative led by the U.S. National Science Foundation aims to stand up AI education and literacy coordination hubs in each U.S. state and territory.
91% of College Grads Used AI in 2026 - #134
Today’s article is from the Yale Daily News. Their senior survey for the class of 2026 came back with ninety-one percent of seniors saying they’ve used AI for schoolwork. That isn’t a usage stat anymore. That’s saturation.
KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing
arXiv:2605.23933v1 Announce Type: new Abstract: Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should ideally personalize questions for each student by modeling the student's knowledge state and generating questions that provide the greatest learning benefit. However, few existing EQG approaches are able to achieve such fine-grained personalization. In this paper, we explore how EQG can benefit from knowledge tracing (KT), which models students' knowledge states based on historical performance and predicts future performance. We propose KT4EQG, a personalized EQG framework that generates effective questions for individual students under the guidance of a KT model. Specifically, KT4EQG seeks to maximize a student's potential improvement in overall knowledge mastery by leveraging the KT model to select the most suitable knowledge concept for the student to practice. An LLM-based question generator is then trained to produce a question faithfully grounded in the selected concept. Experimental results on XES3G5M and MOOCRadar show that KT4EQG consistently generates more effective questions than methods with limited or no personalization.
Generative AI as a Design Variable: An Evidence-Centered Framework for Principled Governance in STEM Assessment
arXiv:2605.24837v1 Announce Type: new Abstract: Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted GenAI access enables task outsourcing that undermines the validity of traditional assessments; blanket prohibitions are difficult to enforce, may push use underground, and do little to prepare students for workplaces where GenAI-supported workflows are increasingly common. This paper addresses this dilemma by proposing a framework grounded in Evidence-Centered Design (ECD) that treats GenAI as a design variable within the assessment argument rather than an external threat to it. The framework analyzes how GenAI reshapes the student model, evidence model, and task model, and uses this analysis to articulate three principled governance stances. Restrict is warranted when GenAI would contaminate the inferential link between student work products and targeted unaided proficiency. Scaffold is warranted when bounded GenAI support can support peripheral demands without revealing the target construct, preserving inferential interpretability. Require is warranted when the target construct is disciplinary AI interaction competency and tasks can be designed to elicit process artifacts, including prompts, critiques, and revisions, that make student reasoning observable, scorable, and distinguishable from AI-generated output. This framework specifies when to restrict, scaffold, or require GenAI use in STEM assessment. We present two task designs deployed in an introductory physics course and demonstrate that disciplinary AI interaction competencies are observable in student response artifacts and can be scored using defensible rubrics grounded in student data and expert knowledge. By situating GenAI governance within validity arguments, the framework offers actionable guidance for preserving learning integrity while supporting authentic preparation for AI-enabled professional environments.
US students on why they booed their pro-AI graduation speakers: ‘They’re not reading the room’
Recent college grads are not very fond of commencement speakers hyping up a technology they see as a threat to their career prospects When Jacob Pagel graduated from Middle Tennessee State University this spring, predictions about artificial intelligence already had him questioning the value of his degree. Then a music executive started preaching about AI’s transformative power during a commencement speech. “This industry will change on you in a heartbeat. It has already changed more in the last 10 years than in the 50 years prior … AI is rewriting production as we sit here,” said Scott Borchetta, CEO of the record label Big Machine. After a few stray boos from graduates, he doubled down: “Deal with it.” Continue reading...
The Future Of Work Is About Skills, Not Jobs
Workforce experts, policymakers, and economists want to know how AI is reshaping the skills workers need and whether institutions are prepared to respond.
AI job losses are increasing. Are training programs the answer?
From Atlanta Technical College to statewide data, Georgia faces AI-driven job losses even as training expands. Experts question if skills equal jobs.
Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning
arXiv:2605.23925v1 Announce Type: new Abstract: Intelligent tutoring systems increasingly provide automated feedback on student work, but robust feedback requires assessing reasoning, not only final answers. We study a failure mode we call the correct answer trap (CAT): models under-detect misconceptions when students reach a correct answer via flawed reasoning. Analysing real student responses from the Eedi mathematics platform, we show that 71% of these failures concentrate in just two question types, both sharing a common structure where flawed reasoning happens to produce the correct numerical answer. Comparing a fine-tuned T5 with a frontier large language model, we find that improved capabilities reduce but do not eliminate the problem (84% vs 57% detection accuracy). Even the best-performing model generates roughly four false alarms for every genuine detection, making stand-alone screening impractical at realistic class sizes. Our findings demonstrate that high overall accuracy can mask critical failures in reasoning assessment, and that careful analysis of student reasoning still benefits from human judgment.
The Pope’s AI Encyclical: The Most Serious Thinking on AI and Education to Date
This is not a short statement, a technology policy, or a set of classroom guidelines. It is a major institutional document, running to roughly 250 paragraphs across five chapters, that treats AI as a civilizational turning point — one that will reshape work, truth, power, human dignity, democracy, and the formation of young people.
Defining AI Fatigue in Academic Contexts: Dimensions, Indicators, and a Stage-Based Model Using Grounded Theory
arXiv:2605.23123v1 Announce Type: new Abstract: The integration of AI tools in academic settings has introduced a distinct form of strain that existing frameworks like technostress and digital fatigue have not yet fully addressed. This study develops a conceptual model and identifies the dimensions that define AI fatigue as a form of strain arising from sustained academic use of AI tools. Using grounded theory analysis of open-ended responses from 1,054 university students across three universities in the Philippines, the study examined the cognitive, motivational, emotional, physical, and attentional pressures students experienced during AI-supported academic work. Analysis produced five dimensions of AI fatigue, namely Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift, each consisting of two indicators grounded in participant accounts. The findings also yielded the AI Fatigue Model, a stage-based framework that explains how these pressures accumulate and reinforce one another across repeated AI interaction in academic tasks. These contributions establish a conceptual and exploratory foundation for AI fatigue as a distinct construct and provide a basis for future instrument validation, scale development, and cross-contextual inquiry in academic settings where AI now mediates student learning.
Is a college degree is still worth it? Here are 3 things it can teach you that AI can’t do
College can help safeguard employees from having their jobs offshored to India or the Philippines, Carl Benedikt Frey told Fortune.
There’s Never Been a Better Time to Study Computer Science
Even as AI progresses, coders aren’t doomed.
Why College Students Are Booing AI
The sound of a cosmic howl
Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
arXiv:2605.21629v1 Announce Type: new Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes? Self-report surveys show little change, while small-scale behavioral studies report widespread AI use without the scale or duration to measure learning consequences. We address both questions using a ten
AI forces rethink of talent as skills gap widens | Cyprus Mail
Artificial intelligence is no longer only changing the tools people use at work, but it is also reshaping what companies, universities and training centres understand by talent. Indeed, speakers at a Cyprus Seeds panel discussion that took place at the Doers Summit in Limassol this week argued ...
AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems
arXiv:2605.21962v1 Announce Type: cross Abstract: Serious games are widely used for learning and training across domains such as healthcare, defense, and education. Persistent challenges remain, however, including static scenario design, authoring bottlenecks, limited learner modeling, and difficulty implementing meaningful real-time instructional adaptation. Recent advances in artificial intelligence (AI) introduce novel capabilities such as dynamic scenario variation, contextual feedback, adaptive pacing, and learner-state modeling that may help address some of these limitations. At the same time, integrating AI into serious games raises important questions related to validity, transparency, system control, and learner trust. This chapter examines how contemporary AI approaches may support real-time instructional adaptation in serious games. It distinguishes between instructional intelligence, defined as a system's capacity to infer learner knowledge and reason about pedagogically appropriate responses, and adaptivity, defined as the ability to modify instructional actions during interaction. A historical synthesis of adaptive learning systems is presented, tracing developments from early computer-assisted instruction through intelligent tutoring systems (ITS), dynamic difficulty adjustment (DDA), authoring platforms, learning analytics, and recent AI-enabled architectures. Building on this perspective, the chapter discusses how large language models (LLMs), reinforcement learning (RL), and agent-based architectures may contribute to more integrated forms of intelligence and adaptivity in serious games. It also highlights practical and research challenges associated with AI-enabled systems, including explainability, validation, computational cost, and the limited empirical evidence regarding long-term learning outcomes in AI-enabled serious games.
Employers are prioritising AI-ready skills across general, tech industries - The Hindu
As AI becomes central to workforce strategy, Indian employers are prioritising practical, AI-ready skills across both general industries and the technology sector, said Nasscom in a report it prepared in collaboration with Indeed, a global job search and hiring platform based in Texas.
Future of Jobs in AI: India's Emerging Opportunities
CPRG and AI4India report reveals new AI job roles in India, focusing on opportunities beyond displacement. Key insights for workforce transformation.
A school district’s lawsuit against Meta for mental health costs was set for trial next month. Zuckerberg settled
The school district had sought more than $60 million to create a 15-year program it said would help counteract mental health and learning issues.
The Promise of AI Is Not Enough - Leadership Is
What Chen, Chen, & Lin (2020) documented in their review of AI applications in education holds here: outcomes vary dramatically based not on the presence of the technology, but on how intentionally it is integrated into learning. Personalized tutoring systems produce results when embedded in thoughtful instructional design.
CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety
arXiv:2605.21609v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existing safety mechanisms remain largely grounded in adult-centric norms and operationalize safety through refusal-oriented suppression. While such approaches may reduce immediate policy violations, they can also create conversational dead-ends, limit constructive guidance, and fail to address the developmental vulnerabilities inherent in adolescent-AI interactions. We argue that adolescent LLM safety should be framed not solely as a filtering problem, but as a socio-technical, developmentally aligned transformation problem. To operationalize this perspective, we propose Critique-and-Revise-for-Teenagers (CR4T), a model-agnostic safeguarding framework that selectively reconstructs unsafe or refusal-style outputs into ageappropriate, guidance-oriented responses while preserving benign intent. CR4T combines lightweight risk detection with domain-conditioned rewriting to remove risk-amplifying content, reduce unnecessary conversational shutdown, and introduce developmentally appropriate guidance. Experimental results show that targeted rewriting substantially reduces unsafe and refusal-oriented outcomes while avoiding unnecessary intervention on acceptable interactions. These findings suggest that selective response reconstruction offers a more human-centered alternative to refusal-centric guardrails for adolescent-facing LLM systems.
Why coding, AI, and real-world projects are the new foundation of B. Tech education - The Hindu
Explore how coding, AI, and real-world projects are transforming B. Tech education and shaping industry-ready engineers in India.
Council Post: How Leaders Can Assist With Upskilling And Reskilling Amid AI Disruption
Upskilling and reskilling requires a coordinated effort across institutions, employers and educators.
AI upends job market for new college graduates who studied computer science - The Washington Post
New computer science grads are trying to start their careers at a time when many experts predict that AI will make their skills obsolete.
Technology usually creates jobs for young, skilled workers. Will AI do the same?
Research explores whether AI will follow historical trends of technology creating jobs for young, skilled workers.
Design Principles and Observable Indicators for AI-Enabled Pedagogical Accompaniment: Evidence from the Amico Dual-Mode Prototype in Italy and China
arXiv:2605.20665v1 Announce Type: cross Abstract: AI-enabled systems are increasingly introduced into educational contexts, yet their effectiveness depends less on technological sophistication than on the quality of pedagogical mediation, ethical constraints, and context-sensitive design. This paper proposes a replicable framework for AI-enabled pedagogical accompaniment, grounded in a human-in-command approach in which adult responsibility remains central and AI functions as an enabling, non-substitutive infrastructure. Building on the Amico project, we operationalize the concept of a relational bridge as a sequence of micro-mediations that lower the threshold of access to educational relationships and facilitate transitions toward meaningful human interaction with teachers, peers, and communities of practice. The contribution synthesizes a set of design principles, including transparency of system identity and limits, scaffolding toward human contact, maieutic questioning, prevention of dependency dynamics, and data minimization, and maps them to observable indicators suitable for real educational settings. The paper also outlines an initial cross-context exploration of the prototype in Italy and China and discusses how the two interaction modes, AmicoMio (structured, task-oriented) and AmicoTuo (reflective, supportive), can be used as complementary pedagogical mediations. Pilot observations and participant feedback suggested feasibility and perceived usefulness in vocational contexts, motivating the present framework, informing the subsequent doctoral research program, and supporting the proposed collaborative research agenda.
Colleges Are at a Breaking Point
The AI job market has made tuition look like a dubious investment. But it only exposes the deeper identity crisis in American higher education.
Future of Work: Upskilling and Reskilling in the AI Era
Explore the importance of upskilling and reskilling in adapting to AI-driven workplace changes and maintaining workforce relevance.
Why the UK’s AI-powered prosperity hinges on skilling for all - Microsoft UK Stories
While employees are increasingly working like it’s 2026, some organisations are still operating like it’s 2019. Unless businesses, educators, government and the technology sector as a whole work together to build AI capability more broadly across the workforce, the UK risks hampering its ...
Opinion | A Defense of a Liberal Arts Education in the Age of A.I. - The New York Times
Making the case for a “useless” education · Hosted by Ross Douthat
The new college graduation ritual: booing AI
Commencement ceremonies are seeing interruptions as students boo speakers who mention AI. This reflects growing anti-AI sentiment as the technology reshapes the job market and student majors.
Automated Grading of Handwritten Mathematics Using Vision-Capable LLMs
arXiv:2605.19043v1 Announce Type: new Abstract: Automated grading systems have enabled scalable assessment for many response types, but handwritten mathematics remains a barrier due to the complexity of multi-step solutions. Vision-capable large language models (LLMs) offer new opportunities here, yet their reliability in authentic instructional settings remains poorly understood. We present an empirical evaluation of an LLM-based grader for handwritten mathematical work using instructor-defined rubrics. Extending a prior pipeline for typed responses, we integrate transcription and rubric-based evaluation of photographic submissions within a single LLM call, evaluating on student work from two university STEM courses. Comparing AI grading decisions against human-assigned ground truth at the rubric-item level, we observe high overall accuracy, with most errors -- 87\% in the best model -- attributable to transcription failures rather than rubric misapplication. We categorize common error modes, including image quality issues, hallucinated content, and incorrect handling of equivalent expressions. These findings highlight both the promise and limitations of LLM-based grading for handwritten mathematics, providing guidance for system design, prompt refinement, and deployment in educational settings.
Locked Out at 8,000 Miles: Why UK-China Partnership Students Are Suffering
arXiv:2605.19367v1 Announce Type: cross Abstract: University cybersecurity protocols have intensified dramatically in response to rising threats of data breaches, ransomware, and credential theft. While necessary, these measures have created a parallel crisis of accessibility - even for students physically on campus. This paper argues that domestic, on-campus students already face significant barriers: mandatory multi-factor authentication (MFA), device compliance rules, browser and operating system restrictions, and administrative remote-management permissions on personal phones and laptops. However, these difficulties are magnified to near-breaking point in the context of international partnerships, such as the increasingly common UK-China transnational education programmes. For a student in China accessing a UK university's virtual learning environment (VLE) from an 8-hour time difference, with no on-hand IT support during their active hours, the same security architecture becomes functionally disabling. Drawing on testimonies from public forums (Reddit's r/college, r/UniUK, r/Professors), higher education IT help boards, and student accounts from UK-China partnership programmes, this paper documents how over-engineering digital security disproportionately harms remote international learners. We show that while on-campus students can at least visit an IT desk or borrow a library terminal, their counterparts in partner institutions abroad face authentication failures, device lockouts, and unsupported browsers with no real-time remedy. The paper concludes that current university security models assume a co-located, 9-to-5, English-time-zone user - an assumption that fails both domestic students and, catastrophically, international partnership cohorts.