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200 articles
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.
How Far Are We From True Auto-Research?
arXiv:2605.19156v1 Announce Type: new Abstract: Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of how good agent-generated papers actually are. We introduce ResearchArena, a minimal scaffold that lets off-the-shelf agents (Claude Code using Opus 4.6, Codex using GPT-5.4, and Kimi Code using K2.5) carry out the full research loop themselves (ideation, experimentation, paper writing, self-refinement) under only lightweight guidance. Across 13 computer science seeds and 3 trials per agent-domain pair, ResearchArena yields 117 agent-generated papers, each evaluated under three complementary lenses: a manuscript-only reviewer (SAR), an artifact-aware peer review (PR) in which agents inspect the workspace alongside the manuscript, and an human conducted meta-review. Under SAR alone the picture is optimistic: Claude Code obtains the highest score, outperforms Analemma's FARS, and matches the weighted-average human ICLR 2025 submission, suggesting that minimally scaffolded agents can produce papers that look competitive on manuscript-only review. Manual inspection, however, reveals this picture is overstated: SAR scores are poorly aligned with its actual acceptance decisions and reward plausible framing without verifying experimental substance. Under artifact-aware PR scores drop sharply, and manual auditing identifies experimental rigor as the major bottleneck, decomposing into three failure modes (fabricated results, underpowered experiments, and plan/execution mismatch) that are highly agent-dependent: Codex 5%/8% paper-vs-artifact mismatch / fabricated references versus Kimi Code 77%/72%, a $\sim$15$\times$ spread that tracks distinct research personas the agents develop. None of the 117 agent-generated papers reaches the acceptance bar of a top-tier venue. This suggests that we are still gapped from the true auto-research.
Artificial Intelligence: How AI is Transforming the Workforce and Redefining Jobs Globally, ETHRWorld
Artificial Intelligence: Explore how AI adoption is reshaping job roles globally as HR leaders identify the urgent need for continuous learning, adaptability, and the blending of human and AI capabilities in the workplace.
Only one in five say education prepares young people for AI future - Educate magazine
The education system is increasingly expected to prepare young people for an AI-driven jobs market but only one in five people believe it is.
AI Slop or AI-enhancement? Student perceptions of AI-generated media for an English for Academic Purposes course
arXiv:2605.16275v1 Announce Type: new Abstract: Artificial intelligence (AI) retrieval-augmented generation (RAG) tools now enable educators to transform course materials into diverse multimedia at scale. However, it remains unclear whether such AI-generated content functions as a pedagogical scaffold or AI slop: high volume, low quality material. This innovative practice paper reports on the development, implementation, and evaluation of teacher-prompted, AI-generated supplemental materials in an English for Academic Purposes (EAP) course at a Hong Kong Community College. Using primarily Google Notebook LM, the instructor generated videos, podcasts, infographics, and individualized feedback reports from course materials and student work for 106 English as a Foreign Language learners. An explanatory sequential mixed-methods design comprising a survey, semi-structured interviews, and correlation analysis with academic scores was employed to examine students' preferences, perceptions, and learning outcomes. Findings are framed through the Technology Acceptance Model and Cognitive Load Theory. Students rated the materials highly for perceived usefulness and ease of use, and preferred assessment-linked content presented in visual and multimodal formats, particularly videos and infographics. Video preference correlated positively with academic performance; however, higher cognitive load was negatively associated with course grades, indicating that material complexity must be carefully calibrated. Notably, some lower-performing students independently adopted the materials as remedial scaffolds. The practice demonstrates that RAG tools enable scalable personalized feedback that would be less feasible through traditional methods. When aligned with student goals and cognitive principles, teacher-prompted AI generation can meaningfully enhance the EAP learning ecosystem rather than producing AI slop.
Measuring Changes in Instructor Class Design and Student Learning After the Release of Large Language Models (LLMs)
arXiv:2605.16284v1 Announce Type: new Abstract: Student use of Generative AI (GenAI) products in completing their classwork, with or without their professors' knowledge and/or approval, has resulted in substantial shifts in higher education. While GenAI use is widespread, its impact on student study methods, faculty course development, grade reporting, and overall learning is not well documented. This is a mixed-methods, multi-course study using retrospective quantitative analysis, instructor surveys, and anonymous student surveys at a university in the New England region of the United States. This research seeks to identify and document patterns in student and faculty perceptions of, and experiences in, the use of LLMs as a learning tool inside and outside of the university classroom. Alongside quantitative and thematic analysis of both faculty and student survey responses, historical grade data as reported to the university registrar is used to triangulate the phenomenon of learning achievement in pre- and post-LLM eras. It is hoped that this research can serve as a pilot study for a broader set of institutions. Results from this study can inform GenAI policy for professors, universities, and other educational institutions that are trying to maximize student learning in the age of AI.
Generative AI in K-12 Classrooms: A Midyear Implementation Report
arXiv:2605.16277v1 Announce Type: new Abstract: This mid-year report summarizes teacher use of Colleague AI across 12 Washington State school districts from September 1 to December 31, 2025. Produced jointly by Colleague AI and AmplifyLearn.AI at the University of Washington, this report aggregates platform data and district-provided administrative records to provide an early look at how teachers engaged with AI during the first half of the 2025-26 school year. The districts vary in size from small districts with a few thousand students to large districts with up to thirty thousand students. The districts are rural, suburban, and urban. Only a subset of districts were able to provide mid-year administrative data, and findings that link teachers' use of Colleague AI to student characteristics should be interpreted as preliminary signals.
MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
arXiv:2605.16290v1 Announce Type: new Abstract: Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal student ability distribution, overlooking the heterogeneous nature of student misconceptions. We propose a persona-driven framework that replaces theoretical ability sampling with data-driven cognitive profiling. Using student interactions from the EEDI dataset, we identify behavioral personas via latent class analysis (LCA), then condition a large language model (LLM) to simulate response distributions for each persona. These signals are aggregated with topic context and fed into a Ridge Regression model to predict the item response theory (IRT) difficulty parameter. With five-fold cross-validation, our method improves over a recent baseline (MSE: 0.367 to 0.274; R2: 0.525 to 0.686). The discovered personas are interpretable and offer insights into why items are difficult, with potential applications to diagnostic assessment design.
The Recovery Mechanism: Technology, Education, and What Happens When the Pattern Breaks
arXiv:2605.16283v1 Announce Type: new Abstract: For centuries, each new technology has automated some layer of cognitive work and been absorbed by education retreating upward to teach the skills machines could not yet reach. Generative AI may be the first technology to break that pattern, because it now operates at the top of the cognitive ladder, where education has always escaped to. The risk is not that AI replaces teachers but that it replaces the productive struggle through which understanding forms. Drawing on historical analysis, labor economics, and new large-scale data on how students and workers actually use AI, this essay surfaces a paradox: the same technology that augments today's skilled workforce may be quietly eroding the developmental process that produces tomorrow's. Current assessment tools cannot yet distinguish students who are building capacity from those who are losing it. The essay argues this is a measurement problem first and a design problem second, and proposes a research agenda focused on learning outcomes rather than usage patterns. Ultimately, it asks what education should become once AI can perform the cognitive work education was built to develop, and offers directions rather than a destination. Capacities like judgment, character, and epistemic identity have not been central to mainstream educational taxonomies, because earlier technologies did not require education to reach so high.
College Kids Don't Want Your AI
Students are pushing back against the adoption of AI on campuses, with some organizing protests, petitions, and performance art to express their concerns about the technology's effects on their education and job prospects. Bloomberg’s Victor Swezey has more. (Source: Bloomberg)
Evidence of a Cognitive Shift in AI Education: How Students Are Rethinking Human Intelligence?
arXiv:2605.16292v1 Announce Type: new Abstract: Perceptions of intelligence shape how learners evaluate and rely on artificial intelligence (AI) systems. Despite rapid advances in AI capabilities, the impact of sustained exposure to these tools on students' valuation of human intelligence (HI) relative to AI remains underexplored. This paper presents a longitudinal analysis of classroom poll responses collected between 2020 and 2026 in AI-focused undergraduate and MSc courses in computer science. Data from 471 students across technical courses (such as Machine Learning and Deep Graph Learning) and design-oriented courses (such as Design Thinking for AI) reveal four recurring phases: hype, distrust, trust, and dependency. While early responses in 2020 slightly favored AI, a consistent shift toward HI emerged from 2024 onward across all MSc cohorts. By 2026, preference for HI reached 65 percent in a technical course (a 12 percentage-point increase from 2025) and 90 percent in a design-oriented course (a 36 percentage-point increase). These findings suggest a gradual reappraisal of human intelligence as AI becomes a routine tool, with implications for learner autonomy and epistemic agency. We conclude by reflecting on this cognitive shift from favoring artificial intelligence toward prioritizing human intelligence.
Report – nearly half of Irish employers have scaled back entry-level hiring
IrishJobs’ research found that hiring in Ireland is becoming increasingly specific, particularly in the area of AI. Read more: Report – nearly half of Irish employers have scaled back entry-level hiring
AI Reshapes Workforce: College Degrees vs. Skilled Trades Demand
AI's rapid expansion is slowing entry-level hiring for college graduates in vulnerable sectors while accelerating demand for skilled blue-collar workers to build critical infrastructure.
Cassandra Chin on the AI Skills Gap in Tech Education | TFiR
In this exclusive interview with ... the AI skills mismatch baked into university computer science curricula, the methodology and tooling behind her AI First Programming book series, and the practical steps that students and parents can take outside of formal education to close the gap...
Colleges are not keeping up with AI and finance job needs, says Imarticus CEO ahead of IPO - CNBC TV18
Mumbai-based Imarticus Learning, which is planning a ₹1,000 crore IPO in FY27, said AI is rapidly changing hiring trends as employers increasingly prioritise skills over degrees.
Homoglyph-based Adversarial Perturbation of Introductory Computer Science Theory Problems
arXiv:2605.16286v1 Announce Type: new Abstract: Different AI tools such as ChatGPT, Gemini, and Claude are becoming very popular. Although they are helpful for many day-to-day tasks, they can be used in unexpected ways. For example, the learning objectives of a course may not be achieved if students use these tools to solve their homework problems. This paper proposes a simple method to address this issue in the lazy student model. The method uses homoglyph-based adversarial perturbation to first modify the question without changing the semantic meaning of the question. Then a few characters are perturbed by their homoglyphs. Our experimental result shows the theoretical problems of introductory computer science courses can be effectively perturbed. We also propose an interactive tool to conveniently use our method.
AI sparks backlash from new graduates. How deep does the disapproval go? - AOL
Many Americans are signaling disapproval of the technology amid fears that it will eclipse already competitive entry-level jobs.
Gen Z's AI Backlash Is Getting Louder - Business Insider
AI anxiety is growing and, in some cases, it's boiling over into public backlash.
Recent commencement speeches show students are souring on AI. How deep does the disapproval go? - CBS News
Many Americans are signaling disapproval of the technology amid fears that it will eclipse already competitive entry-level jobs.
Opinion | Minimum age rules for AI are bad policy - The Washington Post
Young people are told AI will shape our careers. Why shouldn't we be able to access it?
The New Digital Divide: Agentic AI - by David Bachman
I’m in a panic over the upcoming Fall semester. I’m committed to teaching my students the most current skills, but I can’t. Most of them don’t have access to what they need to learn: Agentic AI . AI agents are semi-autonomous systems that accomplish user-specified goals.
King’s College London AI work study finds job fears rising | ETIH EdTech News — EdTech Innovation Hub
AI in education, edtech AI tools, and workforce skills are in focus as King’s College London finds UK concern over AI job losses, student preparedness, and entry-level roles. ETIH edtech news covers the new AI and work tracker, employer adoption, university readiness, and retraining.
The UK must embrace its libraries in the age of AI
As stewards of vast quantities of data, the sector could play a critical role in fuelling the digital economy
As AI meets science, what is in store for the future of research?
Sorin MS Krammer of the University of Southampton explores the issues created by automated academic papers. Read more: As AI meets science, what is in store for the future of research?
Can colleges still deliver in the age of AI? One Ivy League school is investing $30 million to improve career outcomes
College students are increasingly worried that artificial intelligence will upend their future career plans.
America’s business schools are dangling discounts to win back students as AI panic sets in - The Economic Times
American business schools are slashing tuition fees and offering scholarships. This move comes as professionals reconsider the value of traditional MBA degrees due to artificial intelligence. Many are opting for shorter, flexible courses to gain AI skills while staying employed.
The AI Era Is Reordering The 4 Paths Of Business Education
This is the educational gap business schools fail to address. Instead of teaching founders how to pitch investors, education should focus on the process to find the strategic fit and skills to scale operations before dilution occurs or to avoid VC entirely. This focus shifts demand toward programs teaching entrepreneurs how to: ... Deploy capital strategically. The existential question for business schools is not whether AI ...
Training Data
More college students are getting A's, thanks to AI.
Adesua: Development and Feasibility Study of an AI WhatsApp Bot for Science Learning in West Africa
arXiv:2605.15376v1 Announce Type: cross Abstract: Sub-Saharan Africa faces persistently high student-teacher ratios and shortages of qualified teachers, limiting students' access to personalized learning support and formative assessment. To address this challenge, we present Adesua, a WhatsApp-based AI Teaching Assistant for science education that extends the Kwame for Science platform. Adesua leverages WhatsApp's widespread adoption in Africa to provide accessible, curriculum-aligned learning support for Junior High School (JHS) and Senior High School (SHS) students across West Africa. The system integrates curated textbooks and 33 years of national examination questions with generative AI to enable conversational question answering and automated assessment with feedback via a WhatsApp bot. Students can ask science questions, take timed or untimed multiple-choice tests by topic or exam year, and receive instant grading and detailed explanations of correct and incorrect responses. A 6-month feasibility deployment in 2025 had 56 active users in Ghana, including students and parents. Quantitative evaluation showed a high perceived usefulness, with a helpfulness score of 93.75\% for AI-generated answers, albeit with a small number of ratings (n=16). These preliminary results provide a basis for more extensive future evaluation of a WhatsApp-based AI assistant to assess its potential to offer scalable, low-cost personalized learning support and formative assessment in resource-constrained educational contexts.
Throw Away The Digital Adoption Playbook. AI Is A Behavior Problem
Four years advising schools on AI adoption has taught me that the digital playbook everyone is still running was built for a problem AI doesn't have. Here's what works.
Business schools move beyond the basics to teach collaboration with AI
Executive education increasingly focuses on decision-making amid shifting technological capabilities
Little Impact of ChatGPT Availability on High School Student Test Score Performance
arXiv:2605.08812v2 Announce Type: replace Abstract: In educational settings, AI can be used as a learning aid, but can also be used to avoid schoolwork, thereby passing classes while learning little. Many existing studies on the impact of AI on education focus on AI use in controlled settings or with specialized tools. In this paper, the dropoff in ChatGPT activity during non-school summer months
Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education
arXiv:2605.15850v1 Announce Type: new Abstract: In recent years, generative AI (GenAI) in educational settings has become ubiquitous in students' daily lives, despite its potential to induce over-reliance, metacognitive disengagement, and diminished learning when used unrestrictedly. While most prior research has thus focused on how to pedagogically scaffold its usage, the question of when to allow off-the-shelf GenAI remains understudied and lacks pedagogically grounded empirical investigation. We treat access timing itself as a form of implicit scaffolding and operationalize it through a reinforcement learning (RL) agent that decides when students should access GenAI, with a reward function grounded in metacognitive theory, cognitive load theory, and productive failure. In a mixed-methods controlled lab study with N=105 participants, we compared the agent's effect on learning gains and metacognitive engagement to unrestricted and fully restricted use. Results show that strategically timed GenAI access under the reinforcement learning condition improved objective post-test performance and metacognitive accuracy compared with unrestricted access, while reducing task errors and time on task relative to complete withholding, all without the need for explicit metacognitive prompts or structured scaffolding. However, no between-condition differences emerged on self-reported metacognitive awareness. Overall, timing of GenAI access therefore is a tractable, theoretically grounded, and scalable pedagogical paradigm that improves over completely unrestricted and withheld access, compatible with off-the-shelf tools and potentially low adoption barrier. This opens up a new research area that explores how access timing can be facilitated by educators and implemented in human-AI learning system design.
As more jobs demand AI skills, some colleges may fall short in prepping students: 'Why would we train them using the skills of yesterday?'
Colleges offering AI degrees and courses are just the beginning. Schools can take broader steps to prepare students for AI's impact on the workforce.
Eskwai for Students: Generative AI Assistant for Legal Education in Ghana
arXiv:2605.15380v1 Announce Type: cross Abstract: Recent advances in generative AI have shown their potential to be leveraged for legal education. Yet, work on the development and deployment of such systems for legal education in the Global South is limited. In this work, we developed Eskwai for Students, a generative AI assistant to help law students with their legal education. Eskwai for Students is a retrieval augmented generation (RAG) system that provides answers to a wide range of legal questions for law students grounded in a curated database of over 12K case laws and 1.4K legislation in Ghana. We deployed Eskwai for Students in a longitudinal study of 30 months (2.5 years) used by 3.1K law students in Ghana who made 32K queries. We evaluated the helpfulness of our AI, and provided insight into the kinds of queries law students submit to this generative AI tool, which raises some ethical concerns. This work contributes to an understanding of how law students in the Global South are using generative AI for their studies and the ways it could be leveraged responsibly to advance legal education.
Third of university students in Great Britain think AI job losses will cause social unrest, poll finds
Tracker of attitudes towards artificial intelligence also finds almost half of the public would prefer to avoid it One in three university students think AI will wipe out jobs so rapidly it will trigger civil unrest, according to a survey by King’s College London (KCL). Students are among the heaviest users of AI, the poll found, with 77% using it at least a few times a month – compared with 46% of workers – and 27% using it daily or almost daily. Continue reading...
Canvas hack: is it ever a good idea to pay a ransom, and what happens to the data?
Businesses are advised against paying – but many are prepared to deal to protect users’ privacy After a week of outages, hundreds of millions of students’ data stolen, delayed assignment due dates and school login pages being defaced by hackers, the US tech firm Instructure – which operates the education platform Canvas, used by education providers worldwide – announced it had “reached an agreement with the unauthorised actor” behind the ransomware attack. Experts read the careful language as a sign that a ransom has been paid. The company has not confirmed this. Continue reading...
AI is wiping out entry-level jobs. Here's how colleges can fill the gap | Fortune
As AI automates the tasks that once defined first jobs, higher education must rethink how it delivers real-world experience—before students ever graduate.
Students Are Learning Less and Getting Higher Grades Because of AI, Study Finds
Students Are Learning Less and Getting Higher Grades Because of AI, Study Finds # Students Are Learning Less and Getting Higher Grades Because of AI, Study Finds As universities struggle to adapt to AI, the future of the workforce is on the line. By Ece Yildirim Published May 15, 2026, 12:10 pm ET Reading time 3 minutes © Brandon Bell Read Later Read Later The booming use of generative AI by students is leading to rising grade inflation at universities, according to a working paper published this week by the University of California, Berkeley. There are three ways generative AI can be used by students: augmentation, where the tools perform a supporting role assisting in things like research while the student completes the bulk of the work themselves; reinstatement of new AI-based tasks; or through displacement, where it completely automates the work that a student would otherwise
Euan Blair’s Multiverse hits $2.1bn valuation in AI workforce training push
Education technology start-up raises $70mn in its first fundraising since 2022
UK EdTech Multiverse lands €60 million funding round at €1.8 billion valuation
Multiverse, the British upskilling platform for AI and tech adoption, today announced it has raised €60 million ($70 million) in primary funding to expand across Europe, with the goal of ensuring that AI benefits the workforce, rather than displacing it. The funding was led by Schroders Capital, with participation from existing investors including General Catalyst, […]
AI-generated research papers are overwhelming peer review | The Verge
Journal editors and peer reviewers are being flooded with AI-generated papers that are almost impossible to detect.
Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence
arXiv:2605.14266v1 Announce Type: cross Abstract: Integration of artificial intelligent (AI) agents in higher education is transforming teaching, learning and administrative processes. Although existing AI agents effectively support individual tasks, their implementation remains fragmented and inefficient for handling the complexity of educational institutions. This highlights a significant research gap: the lack of integrated eco-system-level agentic multi-agent AI platform capable of coordinated planning, reasoning, and adaptive decision-making across multiple educational functions. This paper presents a forward-looking perspective on agentic multi-agent AI platform in higher education, consisting interconnected autonomous, goal driven agents that support learning, teaching, and institutional operations. It addresses timely and critical questions: Can agentic AI represent the next generation of intelligent systems in tertiary education? Can they collectively support seamless coordinated operations across teaching, learning and administrative support? To what extent can such systems foster inclusive and equitable learning for diverse learners with special educational needs? To ground this perspective, a thematic analysis of existing literature identifies four dominant themes: task-specific fragmented AI tools, the transition from single-agent to multi-agent systems, limited cross-functional integration, and insufficient focus on inclusivity and accessibility. Findings reveal a clear gap between current AI implementations and the needs of holistic, learner-centered educational ecosystem. The paper synthesizes challenges and outlines future research directions for scalable human-aligned, and inclusive agentic AI platform. The significant contribution is the incorporation of inclusive learning perspectives, highlighting how coordinated agentic multi-agent platform can support diverse learners through adaptive, multimodal interventions.
Modeling AI-TPACK in Practice Insights from Teachers Multi-Agent Workflow Design
arXiv:2605.13906v1 Announce Type: new Abstract: This study investigates teachers design behaviors and cognitive underpinnings when designing multi-agent instructional workflows. Analyzing behavioral logs (N=61), cluster and Markov analyses identified three archetypes: Systematic Optimizers iteratively refining complex architectures; Prolific Creators rapidly prototyping pragmatic tools via scaffolding; and Passive Observers exhibiting polarized expert-novice profiles. Subsequent artifact (n=15) and interview (n=12) analyses reveal AI-TPACK integration emerges from a dynamic interplay of systems thinking, pedagogical beliefs, and self-efficacy, not merely from the possession of discrete knowledge. These findings call for differentiated scaffolding responsive to teachers cognitive-behavioral diversity.
Computational Thinking Development in AI Agent Creation_A Mixed-Methods Study
arXiv:2605.14330v1 Announce Type: new Abstract: This mixed-methods study examined computational thinking (CT) development among 93 pre-high school students in a five-day AI agent creation workshop using CocoFlow, a no-code platform. Integrating pre-post assessments, behavioral logs, and interviews, we investigated CT development and how initial CT levels shape learning trajectories. Results revealed significant improvements in abstract thinking (effect size d = 0.71) and algorithmic thinking (effect size d = 0.70). Hierarchical regression identified iterative testing engagement as a predictor of self-efficacy gains (beta = 0.20, p = 0.05). Notably, students with moderate initial CT levels demonstrated substantially greater gains than both high-CT and low-CT peers, revealing an Optimal Development Zone effect (eta squared = 0.55). Qualitative analysis showed moderate-CT students exhibited adaptive expertise, while high-CT students risked over-engineering and low-CT students struggled with task decomposition. These findings challenge linear learning assumptions and provide evidence for differentiated scaffolding in CT education.
LLMs learn scientific taste from institutional traces across the social sciences
arXiv:2603.16659v3 Announce Type: replace-cross Abstract: Reinforcement-learned reasoning has powered recent AI leaps on verifiable tasks, including mathematics, code, and structure prediction. The harder bottleneck is evaluative judgment in low-verifiability domains, where no oracle anchors reward and the core question is which untested ideas deserve attention. We test whether institutional traces, the record of what fields published, where, and at which tier, can serve as a training signal for AI evaluators. Across eight social science disciplines (psychology, economics, communication, sociology, political science, management, business and finance, public administration), we built held-out four-tier research-pitch benchmarks and supervised-fine-tuned (SFT) LLMs on field-specific publication outcomes. The fine-tuned models cleared the 25 percent chance baseline and exceeded frontier-model performance by wide margins, with best single-model accuracy ranging from 55.0 percent in public administration to 85.5 percent in psychology. In management, evaluated against 48 expert gatekeepers, 174 junior researchers, and 11 frontier reasoning models, the best single fine-tuned model (Qwen3-4B) reached 59.2 percent, 17.6 percentage points above expert majority vote (41.6 percent, non-tied) and 28.1 percentage points above the frontier mean (31.1 percent). The fine-tuned models also showed calibrated confidence: confidence rose when predictions were correct and fell when wrong, mirroring how a skilled reviewer can say "I'm sure" versus "I'm guessing." Selective triage on this signal reached very high accuracy on the highest-confidence subsets in every field. Institutional traces, we conclude, encode a scalable training signal for the low-verifiability judgment on which science depends.
Euan Blair’s edtech Multiverse valued at $2.1bn after $70m raise
Formerly known as WhiteHat, Multiverse was last valued at $1.7bn in 2022 following a $220m raise. Read more: Euan Blair’s edtech Multiverse valued at $2.1bn after $70m raise
Entry-level productivity expectations have increased due to AI, report says | HR Dive
Nearly a third of HR professionals told D2L they’re hiring fewer early career workers and using artificial intelligence to fill in the gaps.
The time to act on AI fluency, workplace transformation is now - IT-Online
Foundational AI models are trained primarily on Internet data — and Africa’s voices, languages, and cultural contexts are significantly underrepresented in that data. This is the word from Linda Saunders, country manager and senior director: solution engineering Africa at Salesforce, grounded ...
MIRACLE_Multi-Agent Intelligent Regulation to Advance Collaborative Learning Environment
arXiv:2605.12923v1 Announce Type: new Abstract: Effective collaboration requires Socially Shared Regulation (SSRL), but students often lack these skills. This study introduces the MIRACLE (Multi-Agent Intelligent Regulation to Advance Collaborative Learning Environment) system, which supports SSRL by orchestrating metacognitive regulation and proactively providing emotional and motivational support. We conducted a quasi-experimental study with 90 fifth-grade students. The experimental group (n=42) used a collaborative platform CocoNote equipped with MIRACLE, while the control group (n=48) used the same platform with a general GPT assistant. Quantitative results show the MIRACLE group achieved significant gains across SSRL phases (Planning, Monitoring, Reflection) and produced higher-quality collaborative artifacts compared to the control group. Qualitative findings indicate students perceived MIRACLE as an effective facilitator for cognitive, regulatory, and emotional support. This study demonstrates that specialized, orchestrated AI systems are more effective than generic AI in enhancing SSRL.
An Activity-Theoretical Approach to Teacher Professional Development in Pedagogical AI Agent Design
arXiv:2605.12934v1 Announce Type: new Abstract: This two-cycle formative intervention study examined why teachers disengage from AI agent creation after professional development - a low engagement paradox - and tested whether systemic redesign could address it. Cycle 1 (N=218) revealed that despite completing comprehensive TPD, 87 percent of teachers ceased creating within three weeks, with behavioral tracking and interview analysis identifying systemic contradictions as the source of psychological need frustration rather than capacity deficits. Cycle 2 (N=26) implemented Cultural-Historical Activity Theory and Self-Determination Theory - driven redesign directly targeting diagnosed contradictions, achieving synchronized enhancement of both capacity and willingness. The findings reframe implementation failure as a rational response to need-thwarting systems and offer a replicable CHAT - SDT diagnostic framework for transformative professional development.
The Great Mismatch: How a Shrinking Workforce, AI, and Labor Reallocation Will Define the Next 15 Years - Indeed Hiring Lab
The US labor market's coming challenge isn't a shortage of workers or jobs — it's a shortage of pathways between them.
AI could put people off tech jobs and hurt the economy, warns Raspberry Pi boss
Eben Upton warns against claims that Artificial Intelligence will destroy vast numbers of computing roles over the coming years.
upGrad Bags Rs 360 Crore for Unacademy Deal and AI Growth | Whalesbook
upGrad raises Rs 360 crore from founder Ronnie Screwvala to acquire Unacademy and invest in AI learning. This funding fuels consolidation in India's competitive edtech market.
Predicting Disagreement with Human Raters in LLM-as-a-Judge Difficulty Assessment without Using Generation-Time Probability Signals
arXiv:2605.12422v1 Announce Type: cross Abstract: Automatic generation of educational materials using large language models (LLMs) is becoming increasingly common, but assigning difficulty levels to such materials still requires substantial human effort. LLM-as-a-Judge has therefore attracted attention, yet disagreement with human raters remains a major challenge. We propose a method for predicting which LLM-generated difficulty ratings are likely to disagree with human raters, so that such cases can be sent for re-rating. Unlike prior approaches, our method does not rely on generation-time probability signals, which must be collected during rating generation and are often difficult to compare across LLMs. Instead, exploiting the fact that difficulty is an ordinal scale, we use a separate embedding space, such as ModernBERT, and identify disagreement candidates based on the geometric consistency of the rating set. Experiments on English CEFR-based sentence difficulty assessment with GPT-OSS-120B and Qwen3-235B-A22B showed that the proposed method achieved higher AUC for predicting disagreement with human raters than probability-based baselines.
Early AI Literacy in Culturally Responsive STEM Outreach for Black Youth
arXiv:2605.12355v1 Announce Type: new Abstract: Persistent inequities in STEM education continue to limit the participation of Black youth in science and technology fields across Canada. Structural barriers, underrepresentation, and limited access to culturally affirming learning spaces can restrict both opportunity and confidence in pursuing STEM pathways. This paper examines Ontario Tech University's Engineering Outreach Black Youth Program as an exploratory, practice-based case study of culturally responsive STEM outreach. The program creates inclusive environments where Black youth engage in hands-on, culturally grounded STEM experiences supported by mentorship, representation, and community connection. Its recent integration of artificial intelligence (AI) literacy reflects a growing recognition that early engagement with emerging technologies may expand access to future STEM learning opportunities. The paper discusses how AI-focused activities were introduced within this outreach model and examines short-term outcomes related to AI knowledge, confidence, and critical awareness. Findings suggest gains across these areas, while highlighting the need for future research to examine longer-term outcomes related to STEM belonging, identity, and persistence.
Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs
arXiv:2605.11155v1 Announce Type: new Abstract: Hybrid human-AI tutoring, where technology and humans jointly facilitate student learning, can be more beneficial than AI-only tutoring. However, preliminary evidence suggests that lower-performing students derive greater benefit from human-AI tutoring than higher-performing students. As such, this study evaluates whether a differentiated tutoring policy can effectively support both groups: human tutors initiate support for lower-performing students, while higher-performing students receive reactive, on-demand support. Using their within-grade median state test scores, we assigned 635 students (grades 5-8) to receive proactive (< median) or reactive ($\geq$ median) tutoring. Using a DiDC design, we compare outcomes across two time periods: fall (AI-only tutoring) and spring (proactive-reactive human-AI tutoring). This quasi-experimental design isolates the effects of proactive-reactive tutoring approaches by comparing the discontinuity in spring outcomes to the fall, where no such discontinuity existed. Using data around the cutoff (Imbens-Kalyanaraman criterion), we find significant overall improvements from human-AI tutoring compared to AI-only baseline: 25% increase in time on task, 36% in skill proficiency, and 61% in academic growth (standardized MAP test). Between proactive and reactive tutoring, we find comparable improvements in time-on-task and skill proficiency. However, proactive tutoring, on average, showed marginally higher MAP growth (75%, p = .065) than reactive tutoring, i.e., proactive tutoring was more beneficial to students farther below the cutoff and helped narrow achievement gaps. Our findings provide evidence that differentiated human-AI tutoring addresses the needs of both groups, offering a practical and cost-effective strategy for scaling hybrid instruction.
Reimagining Assessment in the Age of Generative AI: Lessons from Open-Book Exams with ChatGPT
arXiv:2605.12363v1 Announce Type: new Abstract: Generative AI systems such as ChatGPT challenge traditional assumptions about academic assessment by enabling students to generate explanations, code, and solutions in real time. Rather than attempting to restrict AI use, this study investigates how students actually interact with such systems during formal evaluation. Engineering students were permitted to use ChatGPT during take-home open-book exams and were required to submit interaction transcripts alongside exam solutions. This provided direct observational evidence of reasoning processes rather than relying on self-reported behavior. Qualitative analysis revealed three progressive patterns of use: answer retrieval, guided collaboration, and critical verification. While some students initially copied questions verbatim and received generic responses, many refined prompts iteratively and tested outputs. Some of the strongest evidence of reasoning appeared when students evaluated incorrect or incomplete AI responses, revealing evaluative reasoning through debugging, comparison, and justification. The presence of generative AI shifted the cognitive task of assessment from producing solutions to assessing solution validity. The findings suggest that, in AI-mediated assessment environments, correctness of final answers alone may no longer provide sufficient evidence of comprehension. Instead, competencies such as prompt formulation, verification, and judgment become visible indicators of learning. Transparent integration of AI appeared to reduce focus on rule avoidance and promote self-regulation. Assessments should evolve to evaluate reasoning about solutions rather than independent solution production. Generative AI therefore does not invalidate assessment but has the potential to expose deeper forms of understanding aligned with professional practice.
Structural Change, Employment, and Inequality in Europe: an Economic Complexity Approach
arXiv:2410.07906v2 Announce Type: replace Abstract: Structural change consists of industrial diversification towards more productive, knowledge intensive activities. However, changes in the productive structure bear inherent links with job creation and income distribution. In this paper, we investigate the consequences of structural change, defined in terms of labour shifts towards more complex industries, on employment growth, wage inequality, and functional distribution of income. The analysis is conducted for European countries using data on disaggregated industrial employment shares over the period 2010-2018. First, we identify patterns of industrial specialisation by validating a country-industry industrial employment matrix using a bipartite weighted configuration model (BiWCM). Secondly, we introduce a country-level measure of labour-weighted Fitness, which can be decomposed in such a way as to isolate a component that identifies the movement of labour towards more complex industries, which we define as structural change. Thirdly, we link structural change to i) employment growth, ii) wage inequality, and iii) labour share of the economy. The results indicate that our structural change measure is associated negatively with employment growth. However, it is also associated with lower income inequality. As countries move to more complex industries, they drop the least complex ones, so the (low-paid) jobs in the least complex sectors disappear. Finally, structural change predicts a higher labour ratio of the economy; however, this is likely to be due to the increase in salaries rather than by job creation.
The Division of Understanding: Specialization and Democratic Accountability
arXiv:2604.09871v2 Announce Type: replace Abstract: This paper studies how the organization of production shapes democratic accountability. I propose a model in which learning economies make specialization productively efficient: most workers perform one-domain tasks, while a small set of integrators with cross-domain knowledge keep the system coherent. When policy consequences run across domains, integrators understand them better than specialists. Electoral competition then tilts government policies toward integrators' interests, while low aggregate system knowledge weakens governance and reduces the fraction of public resources converted into citizen-valued services. Labor markets leave these civic margins unpriced, failing to internalize the political returns to system knowledge. Broadening specialists can therefore raise welfare relative to the market allocation. The model speaks to debates on liberal arts education and the effects of AI.
Million Tutoring Moves (MTM): An Open Multimodal Dataset for the Science of Tutoring
arXiv:2605.08092v1 Announce Type: new Abstract: We introduce the Million Tutoring Moves (MTM) project, an open dataset initiative aimed at advancing the science of tutoring through large-scale, reusable, and multimodal interaction data. MTM is developed within the National Tutoring Observatory (NTO), a research infrastructure designed to study authentic tutoring interactions and translate them into actionable insights for research, practice, and AI-powered educational technology development. In this paper, we present the vision behind MTM and describe MTM v1, an initial release consisting of 4,654 math tutoring transcripts from a U.S.-based nonprofit online tutoring platform. MTM v1 serves as a first step toward a broader repository that is safe, open, large-scale, broad-coverage, and multimodal. By making tutoring interactions systematically observable and analyzable, MTM aims to support research on instructional processes, improve tutoring practice, and enable the development of AI systems grounded in real educational interactions.
Understanding Student Effort Using Response-Time Propensities During Problem Solving
arXiv:2605.08943v1 Announce Type: new Abstract: Adaptive learning systems can produce substantial learning gains, yet many students engage for too brief or too superficial a period to benefit. A central obstacle is measuring effort. Effort during multi-step problem solving is rarely directly observed, and common log-based proxies, such as time on task, cannot distinguish between a student working carefully and a student encountering a harder problem. We examine step-to-step response time as a scalable effort signal by modeling trait-like differences in students' typical response timing during tutoring (while adjusting for skill difficulty). Using step-level logs from eight classroom deployments of algebra tutoring systems (2020 to 2023) across six U.S. schools (794 students), we estimate student- and knowledge-component-level propensities using hierarchical models and relate them to learning efficiency, defined as performance improvement per completed solution step. Response-time propensities show moderate to strong stability within students, supporting their use as an individual differences measure beyond correctness. At the same time, their relationship to learning is not uniform but conditional on the learner and context. Slower propensities predict greater learning efficiency for higher-proficiency students, consistent with constructive processing, whereas for lower-proficiency students, slower propensities are weakly related or even negative, consistent with unproductive struggle or idling. These associations are strongest early in practice sequences and attenuate later in the class period, highlighting an actionable window for detecting emerging disengagement and low persistence. Overall, response-time propensities provide a practical way to incorporate temporal process data into learner models and to target adaptive supports when effort is most diagnostic.
Oev and Anad discuss skills gap and workforce training | Cyprus Mail
The committee was chaired by Yangos ... the Education, Training and Young Talent Attraction Committee of Oev. During the meeting, participants discussed the challenges facing the labour market in relation to the existing skills gap and the need to promote targeted actions for workforce upskilling and reskilling. The management of the Human Resource Development Authority presented both its current and future activities, with particular emphasis on programmes aimed at people ...
Teachers' Perceived Benefits and Risks of AI Across Fifty-Five Countries: An Audit of LLM Alignment and Steerability
arXiv:2605.08486v1 Announce Type: new Abstract: Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely on fragmented evidence, as most studies of teachers' perceptions focus on single countries or small samples. This lack of representative cross-national evidence limits both theory building and policy development. At the same time, large language models (LLMs) are increasingly used in research, policy, and teachers' professional workflows, despite limited validation in education. To address these gaps, we conduct a large-scale audit of LLM alignment with teachers' perceptions of AI by combining representative international survey data with systematic model evaluation. Using OECD TALIS data from 55 countries and territories, we measure cross-national variation in teachers' perceived benefits and risks of AI. We then benchmark responses from eight state-of-the-art LLMs across four providers under both general and country-specific prompting, comparing higher- and lower-reasoning models. Results reveal substantial cross-national variation in teacher perceptions that is not reliably reflected in LLM outputs. Models compress country differences, overestimate both benefits and risks, and show limited gains from identity prompting or enhanced reasoning. This misalignment matters because LLM-generated guidance and professional discourse increasingly shape how teachers learn about and discuss AI, potentially influencing trust and future adoption decisions. Our findings caution against treating LLM outputs as substitutes for direct engagement with teachers when informing global AI-in-education initiatives. At the same time, some models (e.g., Gemini 3 Fast) partially capture cross-national ranking patterns, suggesting a complementary role in hypothesis generation and exploratory comparative analysis.
The University AI Didn't Replace -- Rethinking Universities in the AI Era
arXiv:2605.07056v1 Announce Type: new Abstract: Generative artificial intelligence (AI) is reshaping higher education, yet many universities remain in early stages of adoption where AI innovation occurs informally and without institutional recognition. This paper presents a framework describing four levels of AI adoption in universities and illustrates these dynamics through a case study of AI-enabled curriculum initiatives in several units. We contend that the key institutional challenge is moving from isolated innovation to strategic integration, where universities redesign learning around AI-supported reasoning and align policies, workload models, and recognition systems to support educational transformation.
What If AI’s Biggest Impact Isn’t Jobs, But Minds?
In this episode of Merryn Talks Money, Merryn Somerset Webb speaks to Tom Slater, manager of Scottish Mortgage Investment Trust and partner at Baillie Gifford, about his provocative paper, “AI Isn’t Coming for Your Job. It’s Coming for Your Mind,” and why the real risk may be a world that looks more productive while quietly losing the judgement, learning and expertise that make progress possible. They also discuss what that means for the future workforce and smart investing. (Source: Bloomberg)
Vibe coding before the trend
arXiv:2605.07751v1 Announce Type: new Abstract: Early 2025 we ran a series of vibe coding challenges across four different student cohorts. The cohorts included 54 ICT students, 24 digital marketing students, and 7 journalism students at Fontys University of Applied Sciences (Netherlands), and 22 BA Communication students at North-West University (South Africa). From the student reflections, five major patterns emerged. Students reported that AI tools shifted their focus from syntax to higher-order thinking; they also described a skill shift from memorizing to evaluating; they viewed AI proficiency as career-essential; they framed their relationship with AI as partnership rather than replacement; and finally non-technical students showed the strongest appreciation for the accessibility these tools provide. This practitioner report documents what we observed during the classroom experiments, we reflect on how the landscape has shifted in the year since, and shares practical lessons for educators considering similar experiments. We present the observations as what they are: patterns from practice, not proven conclusions, in the beleif that sharing early stage experiences contributes to the overall field of AI and education.
Rakan Tutor Wins €10 Million EU Grant to Expand AI Education Across Southeast Asia
Rakan Tutor has received a €10 million grant from the EU to expand AI education across Southeast Asia, aiming to empower students with practical AI skills.
Cognitive Agent Compilation for Explicit Problem Solver Modeling
arXiv:2605.07040v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for tutoring, feedback generation, and content creation, but their broad pretraining makes them hard to constrain and poor substitutes for controllable learners. Educational systems often require inspectable and editable knowledge states: educators want to know what a system assumes the learner knows, and learners benefit when the system can justify actions in terms of explicit skills, misconceptions, and strategies. Inspired by cognitive architectures, we propose Cognitive Agent Compilation (CAC), a framework that uses a strong teacher LLM to compile problem-solving knowledge into an explicit target agent. CAC separates (i) knowledge representation, (ii) problem-solving policy, and (iii) verification and update rules, with the goal of making bounded problem solving more inspectable and editable in educational settings. We present an early proof of concept implemented with Small Language Models that surfaces key design trade-offs, particularly between explicit control and scalable generalization, and positions CAC as an initial step toward bounded-knowledge AI for educational applications.
Culture Council: The Skills AI Can’t Replace — And Why We Need to Start Teaching Them in Schools
We are entering a moment where the most valuable skills are not the ones we’ve traditionally prioritized.
Renegotiating the Education Social Contract for the Age of AI (SSIR)
Choice, agency, and how to design a learning system where private gain and public good reinforce each other.
EDITORIAL: The growing AI skills gap - Taipei Times
Bringing Taiwan to the World and the World to Taiwan
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
arXiv:2605.07723v1 Announce Type: cross Abstract: Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in
I knew my writing students were using AI. Their confessions led to a powerful teaching moment | Micah Nathan
The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words I have been teaching fiction writing at MIT since 2017. Many of my students last wrote fiction in middle school, and very few have experienced a proper workshop, so at the start of every semester I offer these directions for writer and reader alike: Read the story at least twice. Mark what works and what doesn’t – underline great sentences, flag clunky syntax, gaps in logic and unrealistic dialogue. Ask yourself: does the story work? Why or why not? What could improve it? Answer in a signed letter to the author, attached to their story. Give your honest opinions. Remember that an effective peer review demands close reading of the text accompanied by a boldness of spirit. Continue reading...
Google Launches AI-Assisted Interviews for Junior Developers
Google is piloting a program that integrates AI assistants into software engineering interviews to focus on creative problem-solving.
AI Has Reached Research Mathematics. Not as a Genius. As a Very Strange Junior Collaborator.
The headline is blunt: “The job description is changing.” Tao is not saying mathematicians are about to vanish into a GPU cluster. The Nature piece frames the current moment more carefully: many researchers still think the hype is overdone, but AI has moved in the past year from solving school-level problems to becoming useful in research mathematicians’ daily work.
LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework
arXiv:2605.05410v1 Announce Type: new Abstract: Large-language-model (LLM) graders promise to relieve the grading burden of upper-division STEM courses, but most deployments to date send student work to third-party APIs, violating FERPA and exposing institutions to data risk while requiring substantial assignment modification. We present $\textbf{LaTA}\ (\textit{LaTeX Teaching Assistant})$, a drop-in, open-source autograder that runs entirely on commodity on-premises hardware and assumes a LaTeX-native workflow already adopted by many engineering and physics courses. LaTA implements a four-stage pipeline (ingest, segment, grade, report) using a locally hosted open-weight chain-of-thought LLM grader (gpt-oss:120b) that compares student work to an instructor-authored reference solution and applies a YAML rubric with binary per-item scoring. We deployed LaTA in Winter~2026 in ME 373 (Mechanical Engineering Methods) at Oregon State University, grading every weekly assignment for approximately 200 students on a single Mac Studio at \$0 marginal cost per assignment and 1--3 minutes of wall-clock time per submission, enabling regrading of corrected assignments and greatly expanded TA office hour offerings. The instructor-confirmed grading-error rate held at roughly $0.02$--$0.04\%$ per rubric line item across the term. Relative to the same instructor's previous traditionally-graded cohort, the LaTA-graded cohort outperformed by approximately $11\%$ on the midterm exam and $8\%$ on the final exam, and reported large gains in self-assessed confidence on every stated learning objective ($N = 159$ survey responses, $\Delta \geq +1.49$ Likert points, $p < 10^{-27}$ on every comparison). We release the code under AGPLv3.
The Pedagogy of AI Mistakes: Fostering Higher-Order Thinking
arXiv:2605.05472v1 Announce Type: new Abstract: As generative AI becomes increasingly integrated into higher education, its frequent errors and hallucinations, often seen as limitations, offer a unique pedagogical opportunity. By framing AI as a ``learning companion'' whose imperfect outputs prompt analysis, evaluation, and reflection, we argue that instructors can engage students in the fundamental processes of higher-order thinking. This paper presents a design-oriented study in which an AI-integrated syllabus in a \textit{database design} course deliberately leverages AI's limitations to foster critical thinking and higher-order cognitive skills aligned with Bloom's taxonomy of learning. Using a mixed-methods approach, we examine how structured interaction with AI-generated errors supports metacognitive engagement, reinforces disciplinary rigor, and relates to students' perceived AI literacy and subject-matter competency.
Breaking In and Reaching Out: Networking for Women in Computer Science
arXiv:2605.06195v1 Announce Type: new Abstract: Networking is central to careers in computer science, where a globally distributed and diverse community increasingly collaborates across institutional and geographic boundaries, often in hybrid and remote settings. However, access to effective networking is shaped by structural and personal factors, including geography, funding, language, identity, personality, and caregiving responsibilities. Building on prior work, this workshop focuses on women in computing to examine lived experiences of networking and the barriers they encounter. Through a community-driven discussion grounded in a factor-based framework, the workshop aims to surface overlooked challenges and foster shared understanding. Ultimately, it seeks to inform more inclusive, equitable, and accessible networking practices within the computer science community.
Graduating in the Age of AI? Here are the Fastest-Growing Positions According to LinkedIn
AI is reshaping the labor market for recent college graduates, with the fastest-growing position this year being an AI engineer.
The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
arXiv:2605.05648v1 Announce Type: new Abstract: Current Artificial Intelligence (AI)-based tutoring systems (AI tutors) are primarily evaluated based on the pedagogical quality of their feedback messages. While important, pedagogy alone is insufficient because it ignores a critical question: what do students actually do with the feedback they receive? We argue that AI tutor evaluation should be extended with a behavioral dimension grounded in student interaction data, which complements pedagogical assessment. We propose an evaluation framework and apply it to 10,235 code submissions with corresponding AI tutor feedback from an introductory undergraduate programming course to measure whether students act on tutor feedback and whether those actions are applied correctly. Using this framework to compare two deployed AI tutors across different semesters in a large-scale introductory computer science course reveals substantial differences in student engagement patterns that are not captured by pedagogy-only evaluation. Moreover, these engagement-based behavioral signals are more strongly associated with student perception of helpful feedback than pedagogical quality alone, providing a more complete and actionable picture of AI tutor performance.
Trinity College Dublin and Microsoft reveal Ireland AI skills gap | ETIH EdTech News — EdTech Innovation Hub
Trinity College Dublin and Microsoft Ireland find a widening AI maturity gap between SMEs and large organizations in the AI Economy Ireland 2026 report. ETIH edtech news on AI skills, workforce development, gender confidence gaps, and why formal AI policy drives ten times higher productivity gains.
Stop Automating Peer Review Without Rigorous Evaluation
arXiv:2605.03202v1 Announce Type: new Abstract: Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used to produce paper reviews. We ground this position in an empirical comparison of human- versus AI-generated ICLR 2026 reviews and an evaluation of the effect of automated paper rewriting on different AI reviewers. We identify two critical issues: 1) AI reviewers exhibit a hivemind effect of excessive agreement within and across papers that reduces perspective diversity. 2) AI review scores are trivially gameable through paper laundering: prompting an LLM to rewrite a paper could significantly increase the scores from AI reviewers, demonstrating that LLM reviewers are easy to game through stylistic changes rather than scientific results. However, non-gameability and review diversity are necessary but not sufficient conditions for automation. We argue that addressing the peer review crisis requires a science of peer review automation -- not general-purpose LLMs deployed without rigorous evaluation.
A Dialogue-Based Framework for Correcting Multimodal Errors in AI-Assisted STEM Education
arXiv:2605.04131v1 Announce Type: cross Abstract: Large Language Models (LLMs) are democratizing access to personalized tutoring; however, their effectiveness is hindered by challenges in processing multimodal content, which limits AI's potential to provide equitable, high-quality STEM support. This study evaluates LLM performance on multimodal physics problems, identifies specific failure modes through an empirical error taxonomy, and tests practical interventions designed to overcome multimodal processing limitations. We assessed three publicly available LLMs (Claude, Gemini, and ChatGPT) on multimodal physics problems from the OpenStax database and compared the results with text-only performance. An empirically derived error taxonomy was developed through pilot testing, followed by evaluation of a structured multimodal dialogue intervention. All three models achieved near-ceiling accuracy (96%) on text-only physics problems. Performance declined substantially on multimodal problems, consistent with what we term the Multimodal Interference Effect. Error analysis identified four failure modes: visual processing errors, context misinterpretation, mathematical computational errors, and hybrid errors, with visual processing errors being the most prevalent. The structured dialogue intervention corrected 82% of errors overall; visual processing errors were corrected at 100% across all models. Educators and students can implement these interventions immediately, requiring no model retraining, to improve AI tutoring reliability on image-rich STEM content, advancing equitable access to high-quality learning support.
Guidelines for Designing AI Technologies to Support Adult Learning
arXiv:2605.04616v1 Announce Type: new Abstract: AI-powered educational technologies have demonstrated measurable benefits for learners, but their design and evaluation have largely centered on K-12 contexts. As a result, many AI-supported learning systems remain poorly aligned with the needs, constraints, and goals of adult learners. To better understand how AI systems function in adult education, this paper examines the deployment of several AI learning technologies developed within a multidisciplinary, national research institute in the United States focused on adult learning and online education. Drawing on longitudinal deployment data, we conducted a reflexive thematic analysis to identify recurring challenges and design considerations across systems. These insights were synthesized into a set of 19 design guidelines intended to inform future AI-supported adult learning technologies. We demonstrate the utility of these guidelines through a heuristic evaluation of the deployed systems. Lastly, we present a guideline exploration tool that aids in the ideation of technologies by connecting the guidelines to stakeholder statements surfaced in the analysis process.
Class of 2026 faces tough job market and AI concerns as graduation season approaches
Recent college graduates face a 4-year-high unemployment rate and AI concerns as the Class of 2026 enters a challenging job market.
When it comes to AI and the UK’s NEET crisis digital skills are now a social mobility issue | LBC
The UK’s employment challenge is no longer just about creating jobs; it is about ensuring people have the skills and confidence needed to access them.
UK schools should remove pupils’ online photos as AI blackmail threat grows, say experts
Criminals are manipulating pictures found on school websites and social media to create sexually explicit images UK schools should remove pictures of pupils’ faces from their websites and social media accounts because blackmailers are using them to create sexually explicit images, experts have said. Child safety experts and the UK’s National Crime Agency (NCA) warn that criminals are using AI to manipulate photos of children and then demand cash not to publish them. Continue reading...
The AI scientist: now academic papers can be fully automated, what does this mean for the future of research?
The AI Scientist scans existing ... a full research paper – largely without human involvement. It reasons, fails and revises, just as a junior scientist would. The proof? An AI Scientist academic paper describing “a pipeline for automating the entire scientific process end to end” was accepted by the International Conference on Learning Representations and published in the scientific journal Nature in March 2026, following ...
PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs
arXiv:2605.01123v1 Announce Type: new Abstract: Large language models (LLMs) can provide automated feedback in educational settings, but aligning an LLMs style with a specific instructors tone while maintaining diagnostic correctness remains challenging. We ask how can we update an LLM for automated feedback generation to align with a target instructors style without sacrificing core knowledge? We study how Reinforcement Learning from Human Feedback (RLHF) can adapt a transformer-based LLM to generate programming feedback that matches a professors grading voice. We introduce PERSA, an RLHF pipeline that combines supervised fine-tuning on professor demonstrations, reward modeling from pairwise preferences, and Proximal Policy Optimization (PPO), while deliberately constraining learning to style-bearing components. Motivated by analyses of transformer internals, PERSA applies parameter efficient fine-tuning. It updates only the top transformer blocks and their feed-forward projections, minimizing global parameter drift while increasing stylistic controllability. We evaluate our proposed approach on three code-feedback benchmarks (APPS, PyFiXV, and CodeReviewQA) using complementary metrics for style alignment and fidelity. Across both Llama-3 and Gemma-2 backbones, PERSA delivers the strongest professor-style transfer while retaining correctness, for example on APPS, it boosts Style Alignment Score (SAC) to 96.2% (from 34.8% for Base) with Correctness Accuracy (CA) up to 100% on Llama-3, and Gemma-2. Overall, PERSA offers a practical route to personalized educational feedback by aligning both what it says (content correctness) and, crucially, how it says it (instructor-like tone and structure).
Did US Worker Retraining Reduce Participant Automation Exposure?
arXiv:2605.03767v1 Announce Type: new Abstract: This paper evaluates whether the U.S. Workforce Innovation and Opportunity Act (WIOA) supported American worker resilience to technological automation. Analyzing over 23 million WIOA participation records (2017-2023), we introduce the "Retrainability Index," which measures program outcomes through post-intervention wage recovery and shifts in Routine Task Intensity (RTI). We show WIOA rarely shifts workers into less automation-exposed work, with a significant portion of participants simply returning to their prior field. Successful outcomes driven mostly by wage gains, possibly due to "catch-up" mean reversion, rather than changes in occupation. Outcomes are moderated by a person's prior occupational skill set and area of work, as well as their local economy. We find evidence that employer led programs--notably apprenticeships--are associated with the highest incidence of success. This suggests the United States' existing public active labor market programming can support baseline wage recovery for vulnerable populations, but is not well-equipped to support the large-scale, cross-industry labor transitions.
University of Southern Denmark brings AI supercomputer online in Sønderborg
The University of Southern Denmark (SDU) has announced that its new national AI supercomputer in Sønderborg has been brought online. – Biljana Weber/HPC The supercomputer, dubbed Bitten, was built in partnership with Danfoss and HPE. The facility was designed to serve researchers and students across the Danish university system through UCloud, a sovereign research cloud […]
ChinAI #357
ChinAI #357 discusses the latest developments in AI, including the use of AI in Chinese universities.
A Large-Scale Observational Study on Obtaining Lightweight, Randomized Weekly Student Feedback
arXiv:2605.02281v1 Announce Type: new Abstract: Conventional methods of obtaining student feedback on course experience face a fundamental tradeoff between feedback frequency and quality: as feedback requests become more frequent, participation often declines, and responses become less thoughtful over time. To obtain both timely and thoughtful feedback from students, Kim and Piech (Learning at Scale, 2023) recently proposed a simple, lightweight course feedback mechanism: surveying each student a small number of times per term during randomly selected weeks. Named High-Resolution Course Feedback (HRCF), this method has been shown to elicit feedback that instructors find helpful without imposing excessive burden on students. An important question, however, remains unanswered: is the use of this simple method associated with measurable improvements in students' actual course experiences? We study HRCF use across 103 course offerings, totaling 24,216 student enrollments, over four years from Fall 2021 through Fall 2025, spanning 42 unique computer science courses at an R1 institution. Through a regression analysis of four end-of-term student evaluation items for these courses, we find that first-time use of HRCF is not associated with a measurable change in average student ratings. However, among small- and medium-enrollment (<250 students) course offerings, continued HRCF use is associated with average rating increases of 0.045 to 0.048 points per additional term of use for learning-related items. We observe no statistically significant associations for large-enrollment (250 or more students) course offerings, nor for items measuring instructional quality and course organization. Together, these findings suggest that sustained HRCF use may support improvements in students' learning experiences, but that further design enhancements may be needed to produce measurable improvements in instructional quality and course organization.
The "Astonishing Regularity'' Revisited: Sensitivity of Learning-Rate Estimates to Practice-Sequence Length
arXiv:2605.01690v1 Announce Type: new Abstract: A 2023 \textit{PNAS} study by Koedinger et al. (2023) fit the individual Additive Factors Model (iAFM) to 27 educational datasets and reported an ``astonishing regularity'' in student learning rates: students vary substantially in initial knowledge but learn at remarkably similar rates with practice. We probe a largely unexamined assumption underlying this finding -- that observation length in student log data is ignorable for mixed-effects estimation -- by refitting the iAFM on 26 of the original datasets while systematically truncating practice sequences at various depths, holding the set of students and knowledge components constant. Capping at the first ten opportunities per student-skill pair inflates the median estimated IQR of student learning rates by 75\%; capping at five inflates it by 205\%, with individual datasets ranging from negligible to 17-fold. The magnitude of this sensitivity diverges from what standard estimation theory predicts under ignorable truncation, and the dataset-specific heterogeneity is substantial. Three candidate mechanisms from adjacent literatures could account for the pattern -- informative observation length, functional-form misspecification, and identification weakness from sparse per-pair data -- but observational analysis on these data alone cannot adjudicate among them. We argue that practice sequence length distributions are an unexamined property of mixed-effects estimation on observational learning data, deserving explicit reporting before conclusions about learning-rate heterogeneity are drawn.
Nvidia Billionaire Mark Stevens Gives $200 Million to Alma Mater USC for AI Research
Billionaire Nvidia Corp. director Mark Stevens and his wife Mary are gifting $200 million to the University of Southern California to advance AI research and education across the school.
ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming
Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems remain individual-centric and reactive. This paper introduces ProPACT, a proactive AI-driven adaptive collaborative tutor that treats collaboration itself as the object of instruction. ProPACT constructs a multimodal dyadic learner model based on Joint Visual Attention (JVA), Joint Mental Effort (JME), and individual mental effort, and employs an X...
AcademiClaw: When Students Set Challenges for AI Agents
Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic workflows -- homework, research projects, competitions, and personal projects -- that they found current AI agents unable to solve effectively. Curated from 230 student-submitted candidates...
Education for Career Readiness in the Age of AI
What the research says about education, jobs, AI, and what students will need to succeed as future workers and citizens.
Bangladesh Launches AI Course
Bangladesh is launching the Think AI course to integrate AI literacy into its education system, aiming to bridge the digital skills gap through a public-private partnership model.
Duolingo's growth outlook moderates
Duolingo posted strong first-quarter results but signaled a more measured growth trajectory ahead, as the language-learning app prioritizes user engagement and product improvements.
Struggling to Keep Up? These 15 Edtech Companies Are Using AI to Give Teachers Their Lives Back
Generative AI is transforming the classroom. From reducing teacher burnout to "AI-proofing" careers, meet the 15 innovative companies reshaping the future of education in 2026.
More courses and certifications won't fix India's skills gap
Expectations need recalibration. Students equate degrees with employability, employers expect instant productivity, policymakers push curriculum mandates — all reinforce a flawed assumption.
AI Adoption Among Teachers: Insights on Concerns, Support, Confidence, and Attitudes
arXiv:2605.00343v1 Announce Type: new Abstract: The study examines the adoption of artificial intelligence (AI) tools in education by analyzing the roles of institutional support, teacher confidence, and teacher concerns. It aims to determine whether teacher concerns moderate the relationship between institutional support and two outcomes: teacher confidence and attitudes toward AI adoption. The sample included 260 teachers from the Philippines. Composite scores were calculated for institutional support, confidence, concerns, and attitudes. Moderated multiple regression analysis showed that institutional support significantly predicted both teacher confidence and attitudes toward AI. However, teacher concerns did not significantly moderate these relationships. A follow-up mediation analysis tested whether confidence explains the effect of institutional support on attitudes. Results showed full mediation. The indirect effect was significant based on the Sobel test, and the direct effect became non-significant when confidence was included in the model. This shows that institutional support improves teacher attitudes by increasing their confidence. The study recommends that institutions provide structured and ongoing support to strengthen teacher confidence. Professional development, mentoring, and AI integration in teacher education programs can increase readiness and support effective AI adoption.
Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education
arXiv:2605.00361v1 Announce Type: new Abstract: GenAI systems such as ChatGPT are increasingly discussed in programming education, but the ways in which the research literature conceptualizes and frames their role remain unclear. This chapter applies text mining to publications indexed in a leading academic database to map scholarly discourse on ChatGPT in programming education. Term frequency analysis, phrase pattern extraction, and topic modeling reveal four dominant themes: pedagogical implementation, student-centered learning and engagement, AI infrastructure and human-AI collaboration, and assessment, prompting, and model evaluation. The literature prioritizes classroom practice and learner interaction, with comparatively limited attention to assessment design and institutional governance. Across studies, ChatGPT is positioned both as a learning aid that supports explanation, feedback, and efficiency and as a pedagogical risk linked to overreliance, unreliable outputs, and academic integrity concerns. These findings support responsible integration and highlight the need for stronger assessment and governance mechanisms.
The Real Job Destruction from AI Is Hitting Before Careers Can Start | Yale Insights
Yale’s Jeffrey Sonnenfeld and his co-authors say that the impact of AI can be seen among recent college graduates, who are finding it harder and harder to get that first job. With no entry to the workforce, how will younger people develop the skills and wisdom to lead in the future?
Bangladesh Launches 'Think AI' Course to Boost Digital Skills and Bridge Global Development Gap
Supported by Meta, this new educational initiative aims to integrate AI literacy into the Bangladeshi school system through a public-private partnership.
Can AI-literate students gain edge in the changing US job market?
The data shows that 81% of AI users ... their skills for future progression. For first-generation students, that kind of visibility can be especially valuable in navigating unfamiliar career paths. The responsibility for higher education is to ensure AI tools are accessible, practical, and embedded into learning in a way that connects directly to workforce outcomes, so they support mobility for all rather than widen gaps...
Paranoid parenting in the age of AI
It’s an illusion to think we can robot-proof our kids’ education choices
83% of U.S. job seekers want formal AI training, survey finds - Outsource Accelerator
A new Express Employment Professionals–Harris Poll survey found that 83% of United States job seekers want companies to formally train employees on AI.
Policy-Governed LLM Routing with Intent Matching for Instrument Laboratories
arXiv:2604.26955v1 Announce Type: new Abstract: AI tutoring systems in engineering labs face a tension between providing sufficient assistance and preserving learning opportunities. Existing systems typically offer instructors limited control over assistance timing, content, or cost. This paper describes a routing and governance system for LLM-based lab assistance comprising two components: Routiium, an OpenAI-compatible gateway that manages multiple LLM backends with configurable prompt modifications and usage logging, and EduRouter, a policy-aware routing service that enforces per-lab budgets, approval workflows, and embedding-based question matching. We evaluated the system using trace-driven simulation calibrated from two engineering labs (LED characterization, RC circuit analysis) and a 100-query replay through live models. In simulations, governed policies (P1/P2) increased challenge-alignment index from 0.90 to 0.98 and overlay-adherence score from 0.69 to 0.87 compared to ungoverned operation (P0). The productive-struggle window metric increased from 1.4 to 3.6 simulated turns before high-scaffold hints appeared. In the 100-query replay, EduRouter routed 75% of queries to a local model, reducing token costs by 66% ($0.087 vs. $0.26 for all-premium routing) while maintaining canonical hit rate of 1.0 for the curated 89-intent question bank. We release Routiium, EduRouter, canonical-task tooling, and simulator configurations to support replication and future classroom studies.
Simulating Validity: Modal Decoupling in MLLM Generated Feedback on Science Drawings
arXiv:2604.26957v1 Announce Type: new Abstract: In science education, students frequently construct hand-drawn visual models of scientific phenomena. These drawings rely on a visual structure where information is encoded through visual objects, their attributes, and relationships. Multimodal large language models (MLLMs) are increasingly used to generate feedback on students' hand-drawn scientific models. However, the validity of such feedback depends on whether model claims are grounded in the specific visual evidence of the student drawing. This study uncovers grounding failures, consistent with modal decoupling, in off-the-shelf MLLM feedback, where outputs remain pedagogically plausible in form while contradicting the drawing or treating depicted elements as missing. Using N = 150 middle school drawings from a kinetic molecular theory unit spanning five modeling tasks and three competence levels, we generated N = 300 feedback instances with GPT-5.1. All outputs were coded for four grounding error types: object mismatch, attribute mismatch, relation mismatch, and false absence. Grounding failures were common: 41.3% of feedback instances contained at least one error. An inventory-list-first workflow reduced several error categories and lowered the overall error rate, but it did not resolve the underlying limitation: approximately one in three outputs remained flawed, with false absence as the dominant failure mode. Moreover, feedback that appears visually grounded offered little diagnostic value for identifying invalid instances. The findings indicate that modal decoupling is a substantial limitation and that valid feedback will require grounding mechanisms beyond common prompting strategies.
DeepTutor: Towards Agentic Personalized Tutoring
arXiv:2604.26962v1 Announce Type: new Abstract: Education represents one of the most promising real-world applications for Large Language Models (LLMs). However, conventional tutoring systems rely on static pre-training knowledge that lacks adaptation to individual learners, while existing RAG-augmented systems fall short in delivering personalized, guided feedback. To bridge this gap, we present DeepTutor, an agent-native open-source framework for personalized tutoring where every feature shares a common personalization substrate. We propose a hybrid personalization engine that couples static knowledge grounding with dynamic multi-resolution memory, distilling interaction history into a continuously evolving learner profile. Moreover, we construct a closed tutoring loop that bidirectionally couples citation-grounded problem solving with difficulty-calibrated question generation. The personalization substrate further supports collaborative writing, multi-agent deep research, and interactive guided learning, enabling cross-modality coherence. To move beyond reactive interfaces, we introduce TutorBot, a proactive multi-agent layer that deploys tutoring capabilities through extensible skills and unified multi-channel access, providing consistent experience across platforms. To better evaluate such tutoring systems, we construct TutorBench, a student-centric benchmark with source-grounded learner profiles and a first-person interactive protocol that measures adaptive tutoring from the learner's perspective. We further evaluate foundational agentic reasoning abilities across five authoritative benchmarks. Experiments show that DeepTutor improves personalized tutoring quality while maintaining general agentic reasoning abilities. We hope DeepTutor provides unique insights into next-generation AI-powered and personalized tutoring systems for the community.
Addressing the Reality Gap: A Three-Tension Framework for Agentic AI Adoption
arXiv:2604.27245v1 Announce Type: new Abstract: Generative AI has rapidly entered education through free consumer tools, outpacing the ability of schools and universities to respond. Now a new wave of more autonomous agentic AI systems--with the capacity to plan and act towards goals--promises both greater educational personalization and greater disruption. This chapter argues that successfully navigating these innovations requires balancing three core tensions: (1) Implementation Feasibility, or the practical capacity to integrate AI sustainably into real classrooms; (2) Adaptation Speed, or the mismatch between fast-evolving AI capabilities and the slower pace of educational change; and (3) Mission Alignment, or the need to ensure AI applications uphold educational values such as equity, privacy, and pedagogical integrity. First, we review early evidence of generative and agentic AI in various sectors and in frontline education to illustrate these tensions in context. Then, we present a three-tension framework to guide decision-makers in evaluating and designing AI initiatives across K-12 and higher education. We provide examples of how the framework can be applied to plan responsible AI deployments, and we identify emerging trends--such as curriculum-linked AI agents and educator-informed AI design--along with open research directions. We conclude the chapter with recommendations for educational leaders to proactively engage with the opportunities and challenges of AI, so that this technology can be harnessed to enhance teaching and learning in the decade ahead.
A Discipline-Agnostic AI Literacy Course for Academic Research: Architecture, Pedagogy, and Implementation
arXiv:2604.27225v1 Announce Type: new Abstract: The rapid integration of generative AI into academic workflows demands curricula that equip students not only with tool proficiency but with the critical judgment to use those tools responsibly in scholarly work. Existing offerings cluster around two inadequate poles: technical AI development courses serving narrow specialist audiences, and brief general-literacy interventions that cannot develop the sustained, practice-based competencies rigorous research requires. This paper reports the design, theoretical rationale, and implementation of BSTA 495/395: Getting Started with AI-Assisted Research, developed and delivered at Lehigh University (Spring 2026). The course addresses an underserved gap: the competencies required for rigorous AI-assisted literature review. Its architecture organizes instruction into four sequential modules aligned with the cognitive demands of that task: comprehension of individual papers, construction and validation of knowledge taxonomies, identification of research gaps, and synthesis and production of complete literature reviews. Each module embeds an explicit verification discipline and standardized AI attribution practice. Prerequisite-free and discipline-agnostic, the course enrolls upper-level undergraduates and graduate students across all fields with differentiated assessment expectations. Pre- and post-course survey data from the inaugural offering indicate substantial self-reported confidence gains, with the largest in hallucination detection (d = +1.45), responsible AI use (d = +1.33), and AI attribution practice (d = +2.40), consistent with the course's design emphasis. The course constitutes a replicable model for the emerging genre of AI research literacy curricula.
Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework
arXiv:2604.26958v1 Announce Type: new Abstract: Agentic AI systems capable of autonomous goal setting and proactive intervention introduce new challenges for regulating moral-emotional processes in learning environments. Existing frameworks typically treat emotion as reactive feedback or engagement optimization, overlooking the need for normative regulation across autonomous decision cycles.This paper proposes an ethical emotion regulation framework for agentic AI learning design inspired by Toegye Yi Hwang's moral-emotional philosophy. The Ethical Emotion Feedback System (EEFS) is reconstructed as a five-stage architecture aligned with agentic cycles, articulating stage-specific design principles and scenario classifications.An EEFS Evaluation Instrument is introduced to enable systematic assessment of moral-emotional alignment in agentic AI systems.
Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education
arXiv:2604.26964v1 Announce Type: new Abstract: The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and adaptive approaches to learning, which are often required, are not provided in traditional learning methods. In this article, we present a new educational framework, "Learning to Explain Cybersecurity with Q20 Game", based on explainable AI (XAI), an educational game to enhance interactivity in learning. We propose a novel, game-inspired framework - the Explainable Q20 Cybersecurity Recommender (EQ-20CR), that learns to elicit the minimal set of evidential facts needed to justify cybersecurity defensive action. By casting "Why should I execute this mitigation?" as a 20 questions (Q20) game, a policy-based reinforcement-learning (RL) agent actively queries an environment until it can both (i) recommend the optimal security education and (ii) explain that decision with a concise dialogue trace. The article draws from "Playing 20 Question Game with Policy-Based Reinforcement Learning" [1] and "Learning-to-Explain: Recommendation Reason Determination through Q20 Gaming" [2]. The framework uses a policy-based reinforcement learning (RL) agent that leads the user through a sequence of questions to recognize and articulate a targeted cybersecurity concept, attack vector, or defense strategy. Furthermore, users are gradually exposed to informative questions by the system, revealing complicated, structured way at an adaptive difficulty level. In this paper, we design the architecture, its application to various concepts of cybersecurity through illustrative case studies, and its transformative potential on the training and awareness of cybersecurity recommendations.
The Impact of LLM Self-Consistency and Reasoning Effort on Automated Scoring Accuracy and Cost
arXiv:2604.26954v1 Announce Type: new Abstract: Strategic model selection and reasoning settings are more effective than ensembling for optimizing automated scoring with large language models (LLMs). We examined self-consistency (intra-model majority voting) and reasoning effort for scoring conversation-based assessment items in high school mathematics, evaluating 900 student conversations agains
Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programming
arXiv:2604.27134v1 Announce Type: new Abstract: Generative AI is reshaping higher education programming through vibe coding, where students collaborate with AI via natural language rather than writing code line-by-line. We conceptualize this practice as help-seeking, analyzing 19,418 interaction turns from 110 undergraduate students. Using inductive coding and Heterogeneous Transition Network Ana
Marshall meets Bartik: Revisiting the mysteries of the trade
arXiv:2604.26457v1 Announce Type: new Abstract: We identify a causal effect of top inventor inflows on the patent productivity of local inventors by combining the idea-generating process described by Marshall (1890) with the Bartik (1991) instruments involving the state taxes and commuting zone characteristics of the United States. We find that local productivity gains go beyond organizational boundaries and co-inventor relationships, which implies the partially nonexcludable good nature of knowledge in a spatial economy and pertains to the mysteries of the trade in the air. Our counterfactual experiment suggests that the spatial distribution of inventive activity is substantially distorted by the presence of state tax differences.
Sociodemographic Biases in Educational Counselling by Large Language Models
arXiv:2604.25932v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into educational settings, understanding their potential biases is critical. This study examines sociodemographic biases in LLM-based educational counselling. We evaluate responses from six LLMs answering questions about 900 vignettes describing students in diverse circumstances. Each vignette is systematically tested across 14 sociodemographic identifiers - spanning race and gender, socioeconomic status, and immigrant background - along with a control condition, yielding 243,000 model responses. Our findings indicate that (1) all models exhibit measurable biases, (2) bias patterns partially align with documented human biases but diverge in notable ways, (3) the magnitude of these biases is strongly influenced by the precision of the student descriptions, where vague or minimal information amplifies disparities nearly threefold, while concrete, individualised metrics substantially reduce them, and (4) bias profiles vary substantially across models. These results demonstrate the importance of context-rich and personalised educational representations, suggesting that AI-driven educational decisions benefit from detailed student-specific information to promote fairness and equity.
Culturally Aware GenAI Risks for Youth: Perspectives from Youth, Parents, and Teachers in a Non-Western Context
arXiv:2604.26494v1 Announce Type: cross Abstract: Generative AI tools are widely used by youth and have introduced new privacy and safety challenges. While prior research has explored youth's safety in GenAI within western context, it often overlooks the cultural, religious, and social dimensions of technology use that strongly shape youths digital experiences in countries like Saudi Arabia. To address the gap, this study explores children (aged 7 to 17), parents and teachers interactions with GenAI tools and risk perceptions through non-western lens. Through a mixed methods approach, we analyzed 736 Reddit and 1,262 X(Twitter) posts and conducted interviews with 31 Saudi Arabian participants (8 youth, 13 parents, 10 teachers). Our findings highlight context dependent and relational privacy and safety of GenAI from non-western context which often formed by communal structure and prescribed norms. We found significant risks tied to youths disclosure of personal and family information, which conflict with culturally rooted expectations of modesty, privacy, and honor, particularly when youth seek emotional support from GenAI. These risks further compounded by socio economic factors such as cost-saving practices leading to the use of shared GenAI accounts (e.g.ChatGPT) within families or even among strangers. We provide design implication reflecting on parents and teachers expectation of how youth should use GenAI. This work lays groundwork for inclusive, context sensitive parental controls that adhere to cultural norms and values.
Human-in-the-Loop Benchmarking of Heterogeneous LLMs for Automated Competency Assessment in Secondary Level Mathematics
arXiv:2604.26607v1 Announce Type: new Abstract: As Competency-Based Education (CBE) is gaining traction around the world, the shift from marks-based assessment to qualitative competency mapping is a manual challenge for educators. This paper tackles the bottleneck issue by suggesting a "Human-in-the-Loop" benchmarking framework to assess the effectiveness of multiple LLMs in automating secondary-level mathematics assessment. Based on the Grade 10 Optional Mathematics curriculum in Nepal, we created a multi-dimensional rubric for four topics and four cross-cutting competencies: Comprehension, Knowledge, Operational Fluency, and Behavior and Correlation. The multi-provider ensemble, consisted of open-weight models -- Eagle (Llama 3.1-8B) and Orion (Llama 3.3-70B) -- and proprietary frontier models Nova (Gemini 2.5 Flash) and Lyra (Gemini 3 Pro), was benchmarked against a ground truth defined by two senior mathematics faculty members (kappa_w = 0.8652). The findings show a marked "Architecture-compatibility gap". Although the Gemini-based Mixture-of-Experts (Sparse MoE) models achieved "Fair Agreement" (kappa_w ~ 0.38), the larger Orion (70B) model exhibited "No Agreement" (kappa_w = -0.0261), suggesting that architectural compliance with instruction constraints outweighs the scale of raw parameters in rubric-constrained tasks. We conclude that while LLMs are not yet suitable for autonomous certification, they provide high-value assistive support for preliminary evidence extraction within a "Human-in-the-Loop" framework.
As AI Skills Surge, Entry-Level Jobs Lag
As AI Skills Surge, Entry-Level Jobs Lag April 30, 2026 # As AI Skills Surge, Entry-Level Jobs Lag A new Handshake report suggests colleges have a role to play in helping students navigate the rise of AI in a tight job market. By AI-related skills are appearing more often in internship and full-time job postings, a new Handshake report finds. Mariia Vitkovska/iStock/Getty Images As artificial intelligence becomes embedded in both college classrooms and the job market, a new report from Handshake finds a rapidly closing gap between student adoption of AI tools and employer demand for those skills. The job platform’s Class of 2026 report drew on data collected last month from 1,248 bachelor’s degree students graduating this year from nearly 500 institutions nationwide. It shows AI adoption among seniors has shifted from equally split to nearly universal: 85 percent now report using
4 in 10 Students Say AI Will Influence Their Career Choice
4 in 10 Students Say AI Will Influence Their Career Choice April 30, 2026 # 4 in 10 Students Say AI Will Influence Their Career Choice Students report feeling “uncertain,” “concerned,” “nervous” and “depressed” about how AI will impact their future careers, a new survey finds. Also, high cost of living deters them from pursuing a college degree. By Asked how they feel about AI’s impact on their future career, half of student respondents answered, “Uncertain.” Photo illustration by Justin Morrison/Inside Higher Ed | PhonlamaiPhoto/iStock/Getty Images | ruizluquepaz/E+/Getty Images Nearly half—42 percent—of college-eligible students say that artificial intelligence will influence which career they pursue, and 10 percent report that they have already changed their planned major due to concerns about AI, according to a report released Tuesday. “AI is upending the value equation in hi
Tech For Good - Beyond free initiatives: Rethinking the UK’s AI talent strategy
Faye Ellis, Principal Training Architect at Pluralsight, explores why the UK’s AI talent strategy must evolve beyond free courses to deliver measurable business impact and close the skills gap.
AI in Education: Lessons from India, Europe, and the U.S., ETEducation
AI in Education: Lessons from India, Europe, and the U.S., ETEducation - Higher Education - 6 min read # AI’s new classroom: what India, Europe, and the U.S. can teach each other The article compares India’s rapid AI adoption in education, Europe’s return to traditional learning methods, and the U.S.’ cautious experimentation, arguing that AI in classrooms must balance innovation with human oversight, ethics, fairness, and strong educational safeguards. - Published On Apr 30, 2026 at 10:50 AM IST AI in Education: Innovation vs Human Oversight By Shubhashree S. Iyer and Ritu GuptaIndia’s higher education system is embracing AI-based assessments, Scandinavia is retreating to pen-and-paper, and U.S. universities are cautiously experimenting. Together, these paths reveal how AI in education must be balanced with control.Intro: Three Continents, Three ChoicesWhen Indian higher education
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Curiosity and Metacognition: Towards a Unified Framework for Learning and Education in the Age of AI
arXiv:2604.25648v1 Announce Type: new Abstract: This chapter examines the relationship between curiosity and metacognition as critical drivers of autonomous and self-regulated learning. We synthesize recent research to propose a unified framework integrating behavioral, computational, and psychoeducational dimensions, arguing that curiosity, i.e. the intrinsic drive to acquire new knowledge, relies fundamentally on metacognitive monitoring and control. From an educational perspective, we evaluate interventions designed to enhance curiosity in classroom settings. While promising, our review indicates that these interventions yield mixed results, often proving differentially effective for struggling learners, thereby underscoring the necessity for approaches tailored to individual profiles. Finally, we address the paradigm shift introduced by Generative AI. While Large Language Models (LLMs) offer unprecedented scalability for personalized inquiry, we argue that their default interaction modes pose significant risks to the dynamics of curiosity-driven learning. To mitigate these challenges, we review strategies to transform AI from a potential cognitive shortcut into a powerful partner for sustained epistemic development.
Hands-on PDC in Undergraduate Computing Education
arXiv:2604.25812v1 Announce Type: new Abstract: Parallel and Distributed Computing (PDC) is a critical yet conceptually challenging area of the undergraduate computer science curriculum. While students often encounter these concepts in theory, few gain exposure to experience in real high-performance computing (HPC) environments. Research shows that when students are engaged in project-based learning they retain knowledge more effectively. They also develop a deeper understanding of concepts taught in the classroom. This paper presents a practical assignment in which students engage directly with the University of Florida's HiPerGator supercomputer to implement and benchmark matrix multiplication using Python and C (via POSIX threads and OpenMP). Students navigate batch scheduling, core allocation, and performance tuning, experiences that are rarely accessible at the undergraduate level. We describe the assignment in detail and provide a three-year evaluation across multiple course offerings, highlighting how structured access to real HPC infrastructure can deepen student understanding of parallelism and multithreading.
AI is changing jobs. Is your workforce ready to keep up? | Human Resources Online
Johnathan Sim, Domain Chair, Technology, Innovation & Analytics at Temasek Polytechnic School of Business, outlines how AI is reshaping jobs, why organisations must move beyond basic adoption, and how redesigning work, building AI literacy, and strengthening human skills will drive productivity ...
In Backlash Against Tech in Schools, Parents Are Winning Rollbacks
From Salt Lake City to New York City, parents are demanding more sway over the digital tools that schools give children.
From Prototype to Classroom: An Intelligent Tutoring System for Quantum Education
arXiv:2604.24807v1 Announce Type: new Abstract: Quantum computing instructors face a compounding problem: the concepts are counterintuitive, the mathematical formalism is dense, and qualified faculty are scarce outside a small number of well-resourced institutions. Our prior work introduced a knowledge-graph-augmented tutoring prototype with two specialized LLM agents: a Teaching Agent for dynamic interaction and a Lesson Planning Agent for lesson generation. Validated on simulated runs rather than in a real course, that prototype left open whether more aggressive agent specialization would be needed to handle the full range of quantum education tasks under real student load. This paper answers the three questions that the prototype could not answer. Can agent specialization solve the reliability problem in a domain as technically demanding as quantum information science? Can the system run in a real course, not a demonstration? Does the instructor gain actionable intelligence from the deployment? We present ITAS (Intelligent Teaching Assistant System), a multi-agent tutoring system built around four contributions: a five-module QIS curriculum grounded in Watrous's information-first framework, a Spoke-and-Wheel teaching architecture with quantum-specialized agents, a cloud infrastructure designed for production use and regulatory compliance, and a conversational analytics layer for instructors and content developers. Piloted in a quantum computing course at Old Dominion University, the system supports all three answers: deployment evidence is consistent with specialization addressing the task-boundary failures observed in the prototype, cloud infrastructure supports classroom-scale concurrency at sub-textbook cost, and the analytics agent surfaces curriculum gaps the instructor could not otherwise see.
Barriers and Enablers of Online Instruction in Hospitality Education in the Philippines: An Exploratory Study
arXiv:2604.25047v1 Announce Type: new Abstract: This study examined the barriers and enablers of online instruction in hospitality education. A sequential exploratory design was implemented with hospitality teachers from both public and private higher educational institutions in the Philippines. Thematic analysis of interviews identified four key themes: technological barriers, pedagogical challenges, institutional and personal support, and integration of artificial intelligence (AI). These themes were transformed into survey constructs and tested for reliability. Pedagogical challenges, including difficulties in teaching hands-on subjects and sustaining student engagement, emerged as the most critical concerns. Technological barriers such as unstable internet and limited devices were moderately rated, while institutional and personal support received mixed evaluations. Teachers viewed AI integration as helpful but also expressed caution and emphasized the need for training. Reliability analysis showed acceptable to good internal consistency across constructs. The findings highlight the importance of strengthening pedagogical training, providing clear institutional support, and fostering responsible competence in AI use. Future studies should validate these results with larger and more diverse samples.
Towards the Development of Detection of Learned Helplessness in Mathematics: Design and Data Collection Challenges from a Developing Country Perspective
arXiv:2604.25054v1 Announce Type: new Abstract: This study investigates the challenges in designing, data collection, and implementation of a web-based Tutoring System (TS) for teaching linear equations within a developing country context. Originally designed as an Android app, the system was redeveloped as a web application to facilitate cross-platform access and data collection. This redesign enabled enhanced tracking through interaction logs and included features like problem skipping, hints, difficulty-based problem sequencing, and game modes with adaptable progression (e.g., easy-to-hard, hard-to-easy). The main objective was to document the design and data collection challenges encountered in data collection for the development of a model capable of detecting learned helplessness in students' behaviors while using a web application for solving linear equation. Challenges included outdated devices, unreliable internet, and logistical constraints such as limited session durations and delays in obtaining approvals. Environmental disruptions like class cancellations and curriculum gaps further complicated the process, with only 118 out of 410 students eligible and actively participating. These obstacles highlight the complexities of collecting interaction data for detecting learned helplessness in real-world, resource-constrained educational settings.
Adoption of TikTok as a Learning Tool in Physical Education: Evidence from the Philippines
arXiv:2604.25049v1 Announce Type: new Abstract: This study examines the factors that influence the adoption of TikTok as a learning tool for physical education (PE)-related content among tertiary students in the Philippines. The study applies the Technology Acceptance Model (TAM) and Uses and Gratification Theory (UGT) to assess Information Seeking, Personal Identity, Social Interaction, Entertainment, Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Use (IU). A cross-sectional design and Structural Equation Modeling (SEM) were employed. The sample included 1,075 regular TikTok users with an average age of 19 years, the majority of whom were female. The analysis revealed that PU and PEOU were the strongest predictors of IU TikTok for PE related content. The results indicate that TikTok provides an engaging and accessible medium that supports active learning and participation in PE. The study offers empirical evidence from the Philippines and contributes to the academic discussion on the role of short-form video platforms in PE.
Coasting Through Class: Learning Opportunity Loss from Practice Avoidance During Individual Seatwork
arXiv:2604.25014v1 Announce Type: new Abstract: Measures of disengagement provide insights into unproductive use of learning opportunities. Although measures of active disengagement, such as gaming the system and mind-wandering, are well studied, loss of practice time due to outright task avoidance remains relatively understudied. The current study addresses this gap by extending existing within-task measures (idle time) with two new session-level measures (delayed start and early stop) to capture loss of practice time due to task avoidance. We characterize the combined lost time as coasted time and the associated behavior as coasting behavior. Using ASSISTments logs (N = 1,425), we find that students dedicate only 40% of available classwork time to math practice and coast through the remaining 60%. Of the coasted time, 36% resulted from delayed starts, 2% from mid-practice idling, and 62% from stopping early. Delayed start and early stop showed moderate temporal stability (G = 0.73 and 0.71, respectively), suggesting that coasting is a consistent behavioral pattern. Even after excluding early stops attributable to assignment completion (i.e., early stop = 0), coasted time remained substantial at 32%. While we observe significant differences in coasting by gender and IEP status, we do not observe them by other demographic factors or school locale. Critically, students who continued working beyond the first assignment completion ("extra effort") performed significantly better on standardized tests. For research, coasting offers a new lens on opportunity loss by combining session-level disengagement with within-task disengagement. For practitioners, our results highlight the need for platform affordances that support sustained engagement and more productive use of available practice time.
Early Academic Capital as the Causal Origin of Dropout in Constrained Educational Systems -- Evidence from Longitudinal Data and Structural Causal Models
arXiv:2604.22772v1 Announce Type: new Abstract: Dropout in higher education is commonly analysed through observable academic events such as course failure or repetition. However, these event-based perspectives may obscure the underlying structural dynamics that shape student trajectories. In this study, we adopt a causal computational social science approach to identify the origins of dropout in a constrained engineering curriculum. Using longitudinal administrative data from 16,868 students who survived to their second active term, and a leakage-free panel design, we estimate the causal effect of early academic capital accumulation on three-year dropout. Treatment is defined as low early progress (passing at most 1 subject by the end of the second term). We employ G-estimation of structural nested mean models, complemented by marginal structural models with inverse probability weighting. We find a large and robust causal effect: low early academic capital increases dropout probability by 25.3 percentage points (G-estimation), closely matched by a 27.4 pp estimate from IPTW models. This effect is approximately twice as large as the estimated direct impact of later academic events such as first-time gateway-course repetition (12.7 pp). These findings suggest that dropout does not originate in isolated academic failures, but in early trajectory misalignment between academic progress and system-imposed temporal constraints. This perspective shifts the focus of intervention from downstream events to early-stage trajectory formation.
‘You feel radicalized’: A Meta AI exec watched agents beat her top workers. Now she’s built a nonprofit to help Gen Z find jobs before they disappear | TechRadar
The entry level job market has been shrunk by AI, and Gen Z need new tools to find work.
How Higher Ed Can Put The Student AI Bill Of Rights To Work
94% of higher ed staff use AI at work. 46% can't name a governing policy. The Student AI Bill of Rights closes the gap if institutions wire it into procurement.
Opinion: 5 AI Moves Leaders Must Make for Next School Year
If this past school year was about adults figuring out how to adapt systems and approaches to AI, the next school year should be about students actually experiencing something better because of the work the adults did.
Cross-Course Generalizability of SRL-Aligned Predictive Models Using Digital Learning Traces
arXiv:2604.22812v1 Announce Type: new Abstract: STEM dropout rates remain high at universities, particularly in computer science programs with theory-intensive courses. Digital learning environments now capture rich behavioral data that could help identify struggling students early, yet the generalizability of data-driven prediction models across courses and institutions remains uncertain. Guided by self-regulated learning (SRL) theory, this study analyzed multimodal digital-trace data from three undergraduate theoretical computer science courses (N1 = 137, N2 = 104, N3 = 148) at two universities. Weekly SRL-aligned digital-trace indicators were modeled using Elastic Net, Random Forest, and XGBoost to evaluate predictive performance over time and across settings, and model calibration both within and across courses. Early prediction of at-risk students was feasible, with SRL-related behaviors such as time management, effort regulation, and sustained engagement emerging as key predictors. While Random Forest achieved the highest in-sample accuracy, Elastic Net generalized more robustly across contexts. Out-of-sample accuracy and calibration declined between institutions with different base rates, underscoring the contextual nature of predictive analytics in higher education. These findings suggest that digital learning traces enable early identification of at-risk students within courses, but generalizing predictive models beyond their original context requires caution, particularly if the at-risk rates differ between contexts.
Gen Z faces rising job fears as former Meta executive launches AI skills nonprofit - Tech Edition
Former Meta executive launches nonprofit to help Gen Z gain AI skills as fears grow over automation and job security.
Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning
arXiv:2604.22770v1 Announce Type: new Abstract: Most digital language learning curricula rely on discrete-item quizzes that test recall rather than applied conversational proficiency. When progression is driven by quiz performance, learners can advance despite persistent gaps in using grammar and vocabulary during interaction. Recent work on LLM-based judging suggests a path toward scoring open-ended conversations, but using interaction evidence to drive progression and review requires scoring protocols that are reliable and validated. We introduce Learning in Blocks, a framework that grounds progression in demonstrated conversational competence evaluated using CEFR-aligned rubrics. The framework employs heterogeneous multi-agent debate (HeteroMAD) in two stages: a scoring stage where role-specialized agents independently evaluate Grammar, Vocabulary, and Interactive Communication, engage in debate to address conflicting judgments, and a judge synthesizes consensus scores; and a recommendation stage that identifies specific grammar skills and vocabulary topics for targeted review. Progression requires demonstrating 70% mastery, and spaced review targets identified weaknesses to counter skill decay. We benchmark four scoring and recommendation methods on CEFR A2 conversations annotated by ESL experts. HeteroMAD achieves a superior score agreement with a 0.23 degree of variation and recommendation acceptability of 90.91%. An 8-week study with 180 CEFR A2 learners demonstrates that combining rubric-aligned scoring and recommendation with spaced review and mastery-based progression produces better learning outcomes than feedback alone.
Lesson for India from the West: AI impact on white-collar jobs
Post-2022 as AI has spread in developed economies, it is leading to another round of polarisation—the middle class jobs are being lost in offices rather than in factories.
When VLMs 'Fix' Students: Identifying and Penalizing Over-Correction in the Evaluation of Multi-line Handwritten Math OCR
arXiv:2604.22774v1 Announce Type: new Abstract: Accurate transcription of handwritten mathematics is crucial for educational AI systems, yet current benchmarks fail to evaluate this capability properly. Most prior studies focus on single-line expressions and rely on lexical metrics such as BLEU, which fail to assess the semantic reasoning across multi-line student solutions. In this paper, we present the first systematic study of multi-line handwritten math Optical Character Recognition (OCR), revealing a critical failure mode of Vision-Language Models (VLMs): over-correction. Instead of faithfully transcribing a student's work, these models often "fix" errors, thereby hiding the very mistakes an educational assessment aims to detect. To address this, we propose PINK (Penalized INK-based score), a semantic evaluation metric that leverages a Large Language Model (LLM) for rubric-based grading and explicitly penalizes over-correction. Our comprehensive evaluation of 15 state-of-the-art VLMs on the FERMAT dataset reveals substantial ranking reversals compared to BLEU: models like GPT-4o are heavily penalized for aggressive over-correction, whereas Gemini 2.5 Flash emerges as the most faithful transcriber. Furthermore, human expert studies show that PINK aligns significantly better with human judgment (55.0% preference over BLEU's 39.5%), providing a more reliable evaluation framework for handwritten math OCR in educational settings.