AI in Education

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Overview

AI in Education develops and evaluates tools such as intelligent tutoring systems, knowledge tracing, adaptive feedback, and large language model tutors grounded in course materials. These systems aim to personalize instruction, assist teachers with tasks like feedback and grading, and expand access to learning support. Researchers assess them through classroom studies, subject-specific benchmarks, learning-outcome measures, and audits of accuracy, bias, privacy, and accessibility. A central tension is that AI can improve performance during assisted tasks without necessarily building durable, independent skills, so effective use depends on pedagogy, human oversight, and evidence from real learning settings.

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265 papers15 papers in the last four weeks, down 42% on the four weeks before. 0.1% of all new papers.

Mar 30, 2026

Tailoring AI-Driven Reading Scaffolds to the Distinct Needs of Neurodiverse Learners

Neurodiverse learners often require reading supports, yet increasing scaffold richness can sometimes overload attention and working memory rather than improve comprehension. Grounded in the Construction-Integration model and a contingent scaffolding perspective, we examine how structural versus semantic scaffolds shape comprehension and reading experience in a supervised inclusive context. Using an adapted reading interface, we compared four modalities: unmodified text, sentence-segmented text, segmented text with pictograms, and segmented text with pictograms plus keyword labels. In a within-subject pilot with 14 primary-school learners with special educational needs and disabilities, we measured reading comprehension using standardized questions and collected brief child- and therapist-reported experience measures alongside open-ended feedback. Results highlight heterogeneous responses as some learners showed patterns consistent with benefits from segmentation and pictograms, while others showed patterns consistent with increased coordination costs when visual scaffolds were introduced. Experience ratings showed limited differences between modalities, with some apparent effects linked to clinical complexity, particularly for perceived ease of understanding. Open-ended feedback of the learners frequently requested simpler wording and additional visual supports. These findings suggest that no single scaffold is universally optimal, reinforcing the need for calibrated, adjustable scaffolding and provide design implications for human-AI co-regulation in supervised inclusive reading contexts.

Soufiane Jhilal, Eleonora Pasqua, Caterina Marchesi +2

Sony Computer Sciences Laboratories, Paris, France · Institut de l’Audition - Institut Pasteur, Paris, France · Université Paris Cité, France +3

AI in Education
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Mar 30, 2026

Mapping data literacy trajectories in K-12 education

Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.

Robert Whyte, Manni Cheung, Katharine Childs +2

Raspberry Pi Foundation, United Kingdom · University of Cambridge, United Kingdom

Computer Science EducationAI in Education
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Mar 30, 2026

Evaluating LLMs for Answering Student Questions in Introductory Programming Courses

The rapid emergence of Large Language Models (LLMs) presents both opportunities and challenges for programming education. While students increasingly use generative AI tools, direct access often hinders the learning process by providing complete solutions rather than pedagogical hints. Concurrently, educators face significant workload and scalability challenges when providing timely, personalized feedback. This study investigates the capabilities of LLMs to safely and effectively assist educators in answering student questions within a CS1 programming course. To achieve this, we established a rigorous, reproducible evaluation process by curating a benchmark dataset of 170 authentic student questions from a learning management system, paired with ground-truth responses authored by subject matter experts. Because traditional text-matching metrics are insufficient for evaluating open-ended educational responses, we developed and validated a custom LLM-as-a-Judge metric optimized for assessing pedagogical accuracy. Our findings demonstrate that models, such as Gemini 3 flash, can surpass the quality baseline of typical educator responses, achieving high alignment with expert pedagogical standards. To mitigate persistent risks like hallucination and ensure alignment with course-specific context, we advocate for a "teacher-in-the-loop" implementation. Finally, we abstract our methodology into a task-agnostic evaluation framework, advocating for a shift in the development of educational LLM tools from ad-hoc, post-deployment testing to a quantifiable, pre-deployment validation process.

Thomas Van Mullem, Bart Mesuere, Peter Dawyndt

AI in EducationGenerative AI in EducationComputer Science EducationLLM EvaluationLLM-as-a-Judge
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Mar 30, 2026

SLOW: Strategic Logical-inference Open Workspace for Cognitive Adaptation in AI Tutoring

While Large Language Models (LLMs) have demonstrated remarkable fluency in educational dialogues, most generative tutors primarily operate through intuitive, single-pass generation. This reliance on fast thinking precludes a dedicated reasoning workspace, forcing multiple diagnostic and strategic signals to be processed in a conflated manner. As a result, learner cognitive diagnosis, affective perception, and pedagogical decision-making become tightly entangled, which limits the tutoring system's capacity for deliberate instructional adaptation. We propose SLOW, a theory-informed tutoring framework that supports deliberate learner-state reasoning within a transparent decision workspace. Inspired by dual-process accounts of human tutoring, SLOW explicitly separates learner-state inference from instructional action selection. The framework integrates causal evidence parsing from learner language, fuzzy cognitive diagnosis with counterfactual stability analysis, and prospective affective reasoning to anticipate how instructional choices may influence learners' emotional trajectories. These signals are jointly considered to guide pedagogically and affectively aligned tutoring strategies. Evaluation using hybrid human-AI judgments demonstrates significant improvements in personalization, emotional sensitivity, and clarity. Ablation studies further confirm the necessity of each module, showcasing how SLOW enables interpretable and reliable intelligent tutoring through a visualized decision-making process. This work advances the interpretability and educational validity of LLM-based adaptive instruction.

Yuang Wei, Ruijia Li, Bo Jiang

Shanghai Institute of Artificial Intelligence for Education, Shanghai, China

Intelligent Tutoring SystemsAI in EducationGenerative AI in EducationUser Modeling
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Mar 29, 2026

Understanding Teacher Revisions of Large Language Model-Generated Feedback

Large language models (LLMs) increasingly generate formative feedback for students, yet little is known about how teachers revise this feedback before it reaches learners. Teachers' revisions shape what students receive, making revision practices central to evaluating AI classroom tools. We analyze a dataset of 1,349 instances of AI-generated feedback and corresponding teacher-edited explanations from 117 teachers. We examine (i) textual characteristics associated with teacher revisions, (ii) whether revision decisions can be predicted from the AI feedback text, and (iii) how revisions change the pedagogical type of feedback delivered. First, we find that teachers accept AI feedback without modification in about 80% of cases, while edited feedback tends to be significantly longer and subsequently shortened by teachers. Editing behavior varies substantially across teachers: about 50% never edit AI feedback, and only about 10% edit more than two-thirds of feedback instances. Second, machine learning models trained only on the AI feedback text as input features, using sentence embeddings, achieve fair performance in identifying which feedback will be revised (AUC=0.75). Third, qualitative coding shows that when revisions occur, teachers often simplify AI-generated feedback, shifting it away from high-information explanations toward more concise, corrective forms. Together, these findings characterize how teachers engage with AI-generated feedback in practice and highlight opportunities to design feedback systems that better align with teacher priorities while reducing unnecessary editing effort.

Conrad Borchers, Luiz Rodrigues, Newarney Torrezão da Costa +2

Carnegie Mellon University · Federal Technological University of Paraná – UTFPR · Instituto Federal Goiano – IF Goiano +1

AI in EducationGenerative AI in EducationEducational Feedback GenerationEducational Assessment
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Mar 28, 2026

Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs

Two of the most socially consequential issues facing today's children are the rise of artificial intelligence (AI) and the rapid changes to the earth's climate. Both issues are complex and contested, and they are linked through the notable environmental costs of AI use. Using a systems thinking framework, we developed an interactive system called Ecoprompt to help children reason about the environmental impact of AI. EcoPrompt combines a prompt-level environmental footprint calculator with a simulation game that challenges players to reason about the impact of AI use on natural resources that the player manages. We evaluated the system through two participatory design sessions with 16 children ages 6-12. Our findings surfaced children's perspectives on societal and environmental tradeoffs of AI use, as well as their sense of agency and responsibility. Taken together, these findings suggest opportunities for broadening AI literacy to include systems-level reasoning about AI's environmental impact.

Aayushi Dangol, Robert Wolfe, Nisha Devasia +3

University of Washington Seattle, WA, USA · Rutgers University New Brunswick, NJ, USA · University of Toronto Toronto, Ontario, Canada

AI LiteracyResponsible AIAI in Education
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Mar 26, 2026

Personalizing Mathematical Game-based Learning for Children: A Preliminary Study

Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners' engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mechanisms to deliver them to appropriate players in a way that matches their learning abilities. To address this challenge, we propose a framework, guided by adaptive learning theory, that uses artificial intelligence (AI) techniques to build a classifier for player-generated levels. We collect 206 distinct game levels created by both experts and advanced players in Creative Mode, a new tool in a math game-based learning app, and develop a classifier to extract game features and predict valid game levels. The preliminary results show that the Random Forest model is the optimal classifier among the four machine learning classification models (k-nearest neighbors, decision trees, support vector machines, and random forests). This study provides insights into the development of GBL systems, highlighting the potential of integrating AI into the game-level design process to provide more personalized game levels for players.

Jie Gao, Adam K. Dubé

McGill University, Montreal, Quebec, Canada

Mathematics EducationGame-Based LearningEducational TechnologyAI in Education
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Mar 26, 2026

Evaluating adaptive and generative AI-based feedback and recommendations in a knowledge-graph-integrated programming learning system

This paper introduces the design and development of a framework that integrates a large language model (LLM) with a retrieval-augmented generation (RAG) approach leveraging both a knowledge graph and user interaction history. The framework is incorporated into a previously developed adaptive learning support system to assess learners' code, generate formative feedback, and recommend exercises. Moerover, this study examines learner preferences across three instructional modes; adaptive, Generative AI (GenAI), and hybrid GenAI-adaptive. An experimental study was conducted to compare the learning performance and perception of the learners, and the effectiveness of these three modes using four key log features derived from 4956 code submissions across all experimental groups. The analysis results show that learners receiving feedback from GenAI modes had significantly more correct code and fewer code submissions missing essential programming logic than those receiving feedback from adaptive mode. In particular, the hybrid GenAI-adaptive mode achieved the highest number of correct submissions and the fewest incorrect or incomplete attempts, outperforming both the adaptive-only and GenAI-only modes. Questionnaire responses further indicated that GenAI-generated feedback was widely perceived as helpful, while all modes were rated positively for ease of use and usefulness. These results suggest that the hybrid GenAI-adaptive mode outperforms the other two modes across all measured log features.

Lalita Na Nongkhai, Jingyun Wang, Adam Wynn +1

Graduate School of Engineering, Kochi University of Technology, Kochi, Japan · Department of Computer Science, Durham University, Durham, UK

AI in EducationGenerative AI in EducationIntelligent Tutoring Systems
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Mar 25, 2026

When AI Meets Early Childhood Education: Large Language Models as Assessment Teammates in Chinese Preschools

High-quality teacher-child interaction (TCI) is fundamental to early childhood development, yet traditional expert-based assessment faces a critical scalability challenge. In large systems like China's-serving 36 million children across 250,000+ kindergartens-the cost and time requirements of manual observation make continuous quality monitoring infeasible, relegating assessment to infrequent episodic audits that limit timely intervention and improvement tracking. In this paper, we investigate whether AI can serve as a scalable assessment teammate by extracting structured quality indicators and validating their alignment with human expert judgments. Our contributions include: (1) TEPE-TCI-370h (Tracing Effective Preschool Education), the first large-scale dataset of naturalistic teacher-child interactions in Chinese preschools (370 hours, 105 classrooms) with standardized ECQRS-EC and SSTEW annotations; (2) We develop Interaction2Eval, a specialized LLM-based framework addressing domain-specific challenges-child speech recognition, Mandarin homophone disambiguation, and rubric-based reasoning-achieving up to 88% agreement; (3) Deployment validation across 43 classrooms demonstrating an 18x efficiency gain in the assessment workflow, highlighting its potential for shifting from annual expert audits to monthly AI-assisted monitoring with targeted human oversight. This work not only demonstrates the technical feasibility of scalable, AI-augmented quality assessment but also lays the foundation for a new paradigm in early childhood education-one where continuous, inclusive, AI-assisted evaluation becomes the engine of systemic improvement and equitable growth.

Xingming Li, Runke Huang, Yanan Bao +3

National University of Defense Technology, Changsha, China · The Chinese University of Hong Kong, Shenzhen, China · University of Oxford, Oxford, United Kingdom

Educational AssessmentAutomated EvaluationAI in EducationGenerative AI in Education
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Mar 24, 2026

Signals Are Not States: Neuro-Symbolic Safeguards for Culturally Aware Classroom AI

Classroom AI systems increasingly infer high-level educational states such as engagement, confusion, collaboration, participation, and instructional quality from multimodal and linguistic signals. In multicultural and multilingual classrooms, such inferences can translate culturally situated behavior into stereotyped claims: silence may be read as disengagement, gaze aversion as inattention, code-switching as low proficiency, or indirect help-seeking as confusion. We argue that stereotype-aware classroom AI should separate observable evidence from culturally loaded interpretation and should treat unsupported construct-level claims as safety risks. We introduce NSCR, a culturally grounded neuro-symbolic framework that converts video, audio, ASR, lesson artifacts, and contextual metadata into typed facts with uncertainty, provenance, and cultural scope, then composes them through executable reasoning and policy constraints. We define a taxonomy of stereotype-prone classroom inferences and propose a benchmark agenda covering culture-conditioned state inference, evidence-grounded claim verification, multilingual and code-switched reasoning, collaboration analysis, counterfactual cultural robustness, and culture-conditioned red-teaming. We further specify metrics for stereotype leakage, unsupported attribution, cultural calibration gaps, abstention under cultural ambiguity, and evidence faithfulness. The contribution is methodological: a concrete framework and evaluation agenda for mitigating stereotyped reasoning in classroom AI, with education as a high-stakes, culturally variable deployment setting.

Sina Bagheri Nezhad

Independent Researcher, Seattle, WA, USA

AI in EducationAI Safety EvaluationCultural Bias in Language ModelsNeuro-Symbolic ReasoningEducational Data Mining
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Mar 21, 2026

Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception

Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding network. We study whether graph-local evidence and credibility-weighted communication can generate persistent distortions in perceived academic standing, even when agents repeatedly receive objective performance signals. We introduce a data-constrained multi-agent framework in which LLM agents operate through individualized subjective graphs that determine peer visibility, evidence access, and interaction opportunities. Agents exchange uncertainty-annotated assessments, evaluate message credibility, and maintain explicit Gaussian belief states updated through Bayesian fusion. We evaluate the framework on 12 middle-school classrooms comprising 482 students, using questionnaire-derived social information and six consecutive examinations. On the Social-Observed subset (n=419), collective ranking error increases from 0.066 \pm 0.008 to 0.124 \pm 0.009 across six epochs despite repeated exam-based anchoring. Ablations associate individualized visibility and LLM-based trust gating with more stable long-horizon behavior, while constrained retrieval primarily safeguards against global-information leakage. Compared with evaluated DeGroot configurations, the proposed framework achieves lower final ranking error; those DeGroot configurations exhibit near-zero terminal opinion diversity. These findings establish subjective-graph LLM agents as a mechanism-oriented framework for data-constrained simulated social perception. Code is available at https://anonymous.4open.science/r/Rashomonomon-0126.

Jinming Yang, Xinyu Jiang, Xinshan Jiao +1

Complex Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · Department of Computer Science, City University of Hong Kong, Hong Kong · School of Health and Medical Technology, Chengdu Neusoft University, Chengdu,2026 China

Multi-Agent LLM SystemsOpinion DynamicsAI in EducationEducationComputational Social ScienceAgent-Based Modeling
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Mar 20, 2026

AI in Work-Based Learning: Understanding the Purposes and Effects of Intelligent Tools Among Student Interns

This study examined how student interns in Philippine higher education use intelligent tools during their OJT. Data were collected from 384 respondents using a structured questionnaire that asked about AI tool usage, task-specific applications, and perceptions of confidence, ethics, and support. Analysis of task-based usage identified four main purposes: productivity and report writing, communication and content drafting, technical assistance and code support, and independent task completion. ChatGPT was the most commonly used AI tool, followed by Quillbot, Canva AI, and Grammarly. Students reported moderate confidence in using AI and applied these tools selectively and ethically during OJT tasks. This indicate that AI tools assist student interns in various OJT activities related to work-readiness. The study suggests that higher education programs include AI literacy and onboarding. Clear policies and fair access to AI tools are important to support responsible use and prepare students for future careers.

John Paul P. Miranda, Rhiziel P. Manalese, Sheila M. Geronimo +5

Pampanga State University, Pampanga, Philippines · University of the East, Manila, Philippines

AI in EducationGenerative AI in EducationAI and Labor
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Mar 19, 2026

From Understanding to Creation: A Prerequisite-Free AI Literacy Course with Technical Depth Across Majors

Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mason University that teaches undergraduates across majors to understand, use, evaluate, and build AI systems. The course is organized around five mechanisms: (1) a unifying conceptual pipeline (problem definition, data, model selection, evaluation, reflection) traversed repeatedly at increasing sophistication; (2) concurrent integration of ethical reasoning with the technical progression; (3) AI Studios, structured in-class work sessions with documentation protocols and real-time critique; (4) a cumulative assessment portfolio in which each assignment builds competencies required by the next, culminating in a co-authored field experiment on chatbot reasoning and a final project in which teams build AI-enabled artifacts and defend them before external evaluators; and (5) a custom AI agent providing structured reinforcement outside class. The paper situates this design within a comparative taxonomy of cross-major AI literacy courses and pedagogical traditions. Instructor-coded analysis of student artifacts at four assessment stages documents a progression from descriptive, intuition-based reasoning to technically grounded design with integrated safeguards, reaching the Create level of Bloom's revised taxonomy. To support adoption, the paper identifies which mechanisms are separable, which require institutional infrastructure, and how the design adapts to settings ranging from general AI literacy to discipline-embedded offerings. The course is offered as a documented resource, demonstrating that technical depth and broad accessibility can coexist when scaffolding supports both.

Amarda Shehu

Professor of Computer Science; Vice President and Chief AI Officer, George Mason University, Fairfax, Virginia, USA

AI LiteracyAI in Education
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Mar 19, 2026

"You've got a friend in me": Co-Designing a Peer Social Robot for Young Newcomers' Language and Cultural Learning

Community literacy programs supporting young newcomer children in Canada face limited staffing and scarce one-to-one time, which constrains personalized English and cultural learning support. This paper reports on a co-design study with United for Literacy tutors that informed Maple, a table-top, peer-like Socially Assistive Robot (SAR) designed as a practice partner within tutor-mediated sessions. From shadowing and co-design interviews, we derived newcomer-specific requirements and added them in an integrated prototype that uses short story-based activities, multi-modal scaffolding and embedded quizzes that support attention while producing tutor-actionable formative signals. We contribute system design implications for tutor-in-the-loop SARs supporting language socialization in community settings and outline directions for child-centered evaluation in authentic programs.

Neil Fernandes, Cheng Tang, Tehniyat Shahbaz +4

University of Waterloo, Waterloo, Canada · United for Literacy, Toronto, Ontario, Canada

Participatory DesignHuman-Robot InteractionSocial RoboticsAssistive RoboticsAI in EducationHuman-AI Co-Creation
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Mar 18, 2026

TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors

Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic chatbot advice or non-scalable teaching center human-human consultations. We present TeachingCoach, a pedagogically grounded chatbot designed to support instructor professional development through real-time, conversational guidance. TeachingCoach is built on a data-centric pipeline that extracts pedagogical rules from educational resources and uses synthetic dialogue generation to fine-tune a specialized language model that guides instructors through problem identification, diagnosis, and strategy development. Expert evaluations show TeachingCoach produces clearer, more reflective, and more responsive guidance than a GPT-4o mini baseline, while a user study with higher education instructors highlights trade-offs between conversational depth and interaction efficiency. Together, these results demonstrate that pedagogically grounded, synthetic data driven chatbots can improve instructional support and offer a scalable design approach for future instructional chatbot systems.

Isabel Molnar, Peiyu Li, Si Chen +5

University of Notre Dame Notre Dame, Indiana, USA

Educational Dialogue SystemsEducational TechnologyConversational AgentsAI in EducationGenerative AI in Education
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Mar 17, 2026

Emotion-Aware Classroom Quality Assessment Leveraging IoT-Based Real-Time Student Monitoring

This study presents high-throughput, real-time multi-agent affective computing framework designed to enhance classroom learning through emotional state monitoring. As large classroom sizes and limited teacher student interaction increasingly challenge educators, there is a growing need for scalable, data-driven tools capable of capturing students' emotional and engagement patterns in real time. The system was evaluated using the Classroom Emotion Dataset, consisting of 1,500 labeled images and 300 classroom detection videos. Tailored for IoT devices, the system addresses load balancing and latency challenges through efficient real-time processing. Field testing was conducted across three educational institutions in a large metropolitan area: a primary school (hereafter school A), a secondary school (school B), and a high school (school C). The system demonstrated robust performance, detecting up to 50 faces at 25 FPS and achieving 88% overall accuracy in classifying classroom engagement states. Implementation results showed positive outcomes, with favorable feedback from students, teachers, and parents regarding improved classroom interaction and teaching adaptation. Key contributions of this research include establishing a practical, IoT-based framework for emotion-aware learning environments and introducing the 'Classroom Emotion Dataset' to facilitate further validation and research.

Hai Nguyen, Hieu Dao, Hung Nguyen +2

Posts and Telecommunications Institute of Technology (PTIT), Km 10 Nguyen Trai Street, Ha Dong District, Hanoi, Vietnam

AI in EducationAffective ComputingEmotion Recognition
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Mar 17, 2026

When AI Agents Learn from Each Other: Insights from Emergent AI Agent Communities on OpenClaw for Human-AI Partnership in Education

The AIED community envisions AI evolving "from tools to teammates," yet most research still examines AI agents primarily through one-on-one human-AI interactions. We provide an alternative perspective: a rapidly growing ecosystem of AI agent platforms where over 167,000 agents participate, interact as peers, and develop learning behaviors without researcher intervention. Based on a month of daily qualitative observations across multiple platforms including Moltbook, The Colony, and 4claw, we identify four phenomena with implications for AIED: (1) humans who configure their agents undergo a "bidirectional scaffolding" process, learning through teaching; (2) peer learning emerges without any designed curriculum, including sharing concrete agent artifacts such as skills, workflows, and reusable routines; (3) agents converge on shared memory architectures that mirror open learner model design; and (4) trust dynamics, reliance risks, and platform mortality reveal design constraints for networked educational AI. Rather than presenting empirical findings, we argue that these organic phenomena offer a naturalistic window into dynamics that can inform principled design of multi-agent educational systems. We sketch an illustrative curriculum design, "Learning with Your AI Agent Tutor," and outline potential research directions and open problems to show how these observations might inform future AIED practice and inquiry.

Eason Chen, Ce Guan, Zhonghao Zhao +5

Carnegie Mellon University, USA · GiveRep Labs, Virgin Islands (British) · Sui Foundation, Cayman Islands

AI in EducationMulti-Agent CollaborationEmergent Behavior in Multi-Agent SystemsAgent Skill Learning
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Mar 17, 2026

Assessing the Pedagogical Readiness of Large Language Models as AI Tutors in Low-Resource Contexts: A Case Study of Nepal's K-10 Curriculum

The integration of Large Language Models (LLMs) into educational ecosystems promises to democratize access to personalized tutoring, yet the readiness of these systems for deployment in non-Western, low-resource contexts remains critically under-examined. This study presents a systematic evaluation of four state-of-the-art LLMs--GPT-4o, Claude Sonnet 4, Qwen3-235B, and Kimi K2--assessing their capacity to function as AI tutors within the specific curricular and cultural framework of Nepal's Grade 5-10 Science and Mathematics education. We introduce a novel, curriculum-aligned benchmark and a fine-grained evaluation framework inspired by the "natural language unit tests" paradigm, decomposing pedagogical efficacy into seven binary metrics: Prompt Alignment, Factual Correctness, Clarity, Contextual Relevance, Engagement, Harmful Content Avoidance, and Solution Accuracy. Our results reveal a stark "curriculum-alignment gap." While frontier models (GPT-4o, Claude Sonnet 4) achieve high aggregate reliability (approximately 97%), significant deficiencies persist in pedagogical clarity and cultural contextualization. We identify two pervasive failure modes: the "Expert's Curse," where models solve complex problems but fail to explain them clearly to novices, and the "Foundational Fallacy," where performance paradoxically degrades on simpler, lower-grade material due to an inability to adapt to younger learners' cognitive constraints. Furthermore, regional models like Kimi K2 exhibit a "Contextual Blindspot," failing to provide culturally relevant examples in over 20% of interactions. These findings suggest that off-the-shelf LLMs are not yet ready for autonomous deployment in Nepalese classrooms. We propose a "human-in-the-loop" deployment strategy and offer a methodological blueprint for curriculum-specific fine-tuning to align global AI capabilities with local educational needs.

Pratyush Acharya, Prasansha Bharati, Yokibha Chapagain +2

AI in EducationLLM EvaluationIntelligent Tutoring SystemsCross-Cultural Language Model Evaluation
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Mar 16, 2026

Customizing ChatGPT for Second Language Speaking Practice: Genuine Support or Just a Marketing Gimmick?

ChatGPT, with its customization features and Voice Mode, has the potential for more engaging and peresonalized ESL (English as a Second Language) education. This study examines the efficacy of customized ChatGPT conversational features in facilitating ESL speaking practices, comparing the performance of four versions of ChatGPT Voice Mode: uncustomized Standard mode, uncustomized Advanced mode, customized Standard mode, and customized Advanced mode. Customization was guided by prompt engineering principles and grounded in relevant theories, including Motivation Theory, Culturally Responsive Teaching (CRT), Communicative Language Teaching (CLT), and the Affective Filter Hypothesis. Content analysis found that customized versions generally provided more balanced feedback and emotional support, contributing to a positive and motivating learning environment. However, cultural responsiveness did not show significant improvement despite targeted customization efforts. These initial findings suggest that customization could enhance ChatGPT's capacity as a more effective language tutor, with the standard model already capable of meeting the learning needs. The study underscores the importance of prompt engineering and AI literacy in maximizaing AI's potential in language learning.

Fanfei Meng

University of Pennsylvania

AI in EducationPersonalized Language Models
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Mar 13, 2026

Human-in-the-Loop LLM Grading for Handwritten Mathematics Assessments

Providing timely and individualised feedback on handwritten student work is highly beneficial for learning but difficult to achieve at scale. This challenge has become more pressing as generative AI undermines the reliability of take-home assessments, shifting emphasis toward supervised, in-class evaluation. We present a scalable, end-to-end workflow for LLM-assisted grading of short, pen-and-paper assessments. The workflow spans (1) constructing solution keys, (2) developing detailed rubric-style grading keys used to guide the LLM, and (3) a grading procedure that combines automated scanning and anonymisation, multi-pass LLM scoring, automated consistency checks, and mandatory human verification. We deploy the system in two undergraduate mathematics courses using six low-stakes in-class tests. Empirically, LLM assistance reduces grading time by approximately 23% while achieving agreement comparable to, and in several cases tighter than, fully manual grading. Occasional model errors occur but are effectively contained by the hybrid design. Overall, our results show that carefully embedded human-in-the-loop LLM grading can substantially reduce workload while maintaining fairness and accuracy.

Arne Vanhoyweghen, Vincent Holst, Melika Mobini +9

Data Analytics Lab, Vrije Universiteit Brussel, 1050 Brussel, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

Automated GradingAI in EducationHuman-in-the-Loop Evaluation
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Mar 12, 2026

ELLA: Generative AI-Powered Social Robots for Early Language Development at Home

Early language development shapes children's later literacy and learning, yet many families have limited access to scalable, high-quality support at home. Recent advances in generative AI make it possible for social robots to move beyond scripted interactions and engage children in adaptive, conversational activities, but it remains unclear how to design such systems for pre-schoolers and how children engage with them over time in the home. We present ELLA (Early Language Learning Agent), an autonomous, generative AI-powered social robot that supports early language development through interactive storytelling, parent-selected language targets, and scaffolded dialogue. Using a multi-phased, human-centered process, we interviewed parents (n=7) and educators (n=5) and iteratively refined ELLA through twelve in-home design workshops. We then deployed ELLA with ten children for eight days. We report design insights from in-home workshops, characterize children's engagement and behaviors during deployment, and distill design implications for generative AI-powered social robots supporting early language learning at home.

Victor Nikhil Antony, Shiye Cao, Shuning Wang +1

Johns Hopkins University, USA

Human-Robot InteractionSocial RoboticsAI in EducationGenerative AI in EducationHuman-Centered AI
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Mar 12, 2026

CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading

Scaling educational assessment with large language models requires not just accuracy, but the ability to recognize when predictions are trustworthy. Instruction-tuned models tend to be overconfident, and their reliability deteriorates as curricula evolve, making fully autonomous deployment unsafe in high-stakes settings. We introduce CHiL(L)Grader, the first automated grading framework that incorporates calibrated confidence estimation into a human-in-the-loop workflow. Using post-hoc temperature scaling, confidence-based selective prediction, and continual learning, CHiL(L)Grader automates only high-confidence predictions while routing uncertain cases to human graders, and adapts to evolving rubrics and unseen questions. Across three short-answer grading datasets, CHiL(L)Grader automatically scores 35-65% of responses at expert-level quality (QWK >= 0.80). A QWK gap of 0.347 between accepted and rejected predictions confirms the effectiveness of the confidence-based routing. Each correction cycle strengthens the model's grading capability as it learns from teacher feedback. These results show that uncertainty quantification is key for reliable AI-assisted grading.

Pranav Raikote, Korbinian Randl, Ioanna Miliou +2

Department of Computer and Systems Sciences, Stockholm University, 164 25 Kista, Sweden

Automated GradingAI in EducationHuman-in-the-Loop EvaluationLanguage Model Calibration
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Mar 12, 2026

Scaling Laws for Educational AI Agents

While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and compute, the scaling behavior of LLM-based educational agents remains unexplored. We propose that educational agent capability scales not merely with the underlying model size, but through structured dimensions that we collectively term the Agent Scaling Law: role definition clarity, skill depth, tool completeness, runtime capability, and educator expertise injection. Central to this framework is AgentProfile, a structured JSON-based specification that serves as the mechanism enabling systematic capability growth of educational agents. We present EduClaw, a profile-driven multi-agent platform that operationalizes this scaling law, demonstrating its effectiveness through the construction and deployment of 330+ educational agent profiles encompassing 1,100+ skill modules across K-12 subjects. Our empirical observations suggest that educational agent performance scales predictably with profile structural richness. We identify two complementary scaling axes -- Tool Scaling and Skill Scaling -- as future directions, arguing that the path to more capable educational AI lies not solely in larger models, but in stronger structured capability systems.

Mengsong Wu, Hao Hao, Shuzhen Bi +5

East China Normal University · Shanghai Innovation Institute

AI in EducationGenerative AI in EducationMulti-Agent LLMsLLM Agent Skill Learning
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Mar 12, 2026

EducaSim: Interactive Simulacra for CS1 Instructional Practice

Role play is a high-impact mode of training that has demonstrated its effectiveness in improving learning outcomes. However, it is difficult to scale to teacher instruction due to its inherent dependency on providing personnel who are both trained and available to facilitate this learning environment. This poses a challenge, especially to massive online courses which may employ and aid hundreds to thousands of novice teachers. In this work, we present EducaSim: a novel framework that uses generative agents to simulate a small-group section for teachers-in-training to practice instruction. EducaSim works by implementing diverse pedagogical-based personas, actual course material, and agent-based architectures constructed for instructional practice to provide a pedagogically rich environment for teachers-in-training to engage in role play learning -- without the costly overhead that comes with it. We share our experiences with constructing and making the tool available for experimental training and preparation in a six-week CS1 course supporting 20,000 students. We found that teachers who engaged generally saw it as a positive experience. We believe that EducaSim is an important step to providing experiential teaching practice at scale for closely-defined settings and has great potential for future applications.

Cameron Mohne, Nicholas Vo, Dora Demszky +1

Stanford University, California, USA

AI in EducationComputer Science EducationLarge Language Model-Based Role-Play SimulationGenerative AI in EducationUser Simulation
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Mar 10, 2026

Beyond Passive Viewing: A Pilot Study of a Hybrid Learning Platform Augmenting Video Lectures with Conversational AI

The exponential growth of AI education has brought millions of learners to online platforms, yet this massive scale has simultaneously exposed critical pedagogical shortcomings. Traditional video-based instruction, while cost-effective and scalable, demonstrates systematic failures in both sustaining learner engagement and facilitating the deep conceptual mastery essential for AI literacy. We present a pilot study evaluating a novel hybrid learning platform that integrates real-time conversational AI tutors with traditional video lectures. Our controlled experiment (N = 58, mean age M = 21.4, SD = 2.8) compared traditional video-based instruction with our AI-augmented video platform. This study employed a sequential within-subjects design where all participants first completed the traditional video condition followed by the AI-augmented condition, providing direct comparisons of learning outcomes. We measured learning effectiveness through immediate post-tests and delayed retention assessments (2-week delay). Results suggest improvements in learning performance: immediate post-test performance showed a large effect size (d = 1.505) with participants scoring 8.3 points higher after AI-augmented instruction (91.8 vs 83.5 out of 100, p < .001). Behavioral analytics revealed increased engagement duration (71.1% improvement with AI tutoring) in the experimental group. This pilot study provides preliminary evidence that conversational AI tutors may enhance traditional educational delivery, suggesting a potential avenue for developing scalable, adaptive learning systems.

Mohammed Abraar, Raj Abhijit Dandekar, Rajat Dandekar +1

Vizuara AI Labs

Educational TechnologyAI in EducationIntelligent Tutoring SystemsConversational AgentsAI Literacy
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Mar 8, 2026

Optimizing LLM Annotation of Classroom Discourse through Multi-Agent Orchestration

Large language models (LLMs) are increasingly positioned as scalable tools for annotating educational data, including classroom discourse, interaction logs, and qualitative learning artifacts. Their ability to rapidly summarize instructional interactions and assign rubric-aligned labels has fueled optimism about reducing the cost and time associated with expert human annotation. However, growing evidence suggests that single-pass LLM outputs remain unreliable for high-stakes educational constructs that require contextual, pedagogical, or normative judgment, such as instructional intent or discourse moves. This tension between scale and validity sits at the core of contemporary education data science. In this work, we present and empirically evaluate a hierarchical, cost-aware orchestration framework for LLM-based annotation that improves reliability while explicitly modeling computational tradeoffs. Rather than treating annotation as a one-shot prediction problem, we conceptualize it as a multi-stage epistemic process comprising (1) an unverified single-pass annotation stage, in which models independently assign labels based on the rubric; (2) a self-verification stage, in which each model audits its own output against rubric definitions and revises its label if inconsistencies are detected; and (3) a disagreement-centric adjudication stage, in which an independent adjudicator model examines the verified labels and justifications and determines a final label in accordance with the rubric. This structure mirrors established human annotation workflows in educational research, where initial coding is followed by self-checking and expert resolution of disagreements.

Bakhtawar Ahtisham, Kirk Vanacore, Rene F. Kizilcec

Cornell University, USA

LLM-Assisted AnnotationAI in EducationLLM ReliabilityMulti-LLM Collaboration
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Mar 4, 2026

Inference Energy and Latency in AI-Mediated Education: A Learning-per-Watt Analysis of Edge and Cloud Models

Immediate feedback is a foundational requirement of effective AI-mediated learning, yet the energy and latency costs of delivering it remain largely unexamined. This study investigates the latency-energy-learning trade-off in AI tutoring through an empirical comparison of two on-device inference configurations of Microsoft Phi-3 Mini (4k-instruct) on an NVIDIA T4 GPU: full-precision FP16 and 4-bit NormalFloat (NF4) quantisation. Both were evaluated under KV-cache-enabled inference across 500 educational prompts spanning five secondary school subject domains. Pedagogical quality was assessed for each of the 1000 generated responses by a hybrid panel of 10 Cambridge International teachers and three frontier AI systems using a four-dimension rubric. We introduce Learning-per-Watt (LpW), a novel metric quantifying pedagogical value per unit of energy over the learner's waiting window. Under realistic deployment, NF4 achieves lower per-inference energy than FP16 (329 J vs. 369 J) but higher latency (13.4 s vs. 9.2 s), yielding a modest FP16 advantage in LpW of 1.33x at a quality difference of 0.19 points. Under cache-disabled inference -- used in offline evaluation but absent from real deployments -- the gap widens to 7.4x, overstating the FP16 advantage by more than fivefold. Quantisation efficiency is hardware-dependent and inference-regime dependent, with significant implications for equitable AI tutoring deployment in low-resource settings.

Kushal Khemani

Billabong High International School, Hadapsar, Pune, 411028, Maharashtra, India.

Power-Efficient InferenceOn-Device Language Model InferenceAI in EducationEnergy-Efficient ML
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Mar 4, 2026

The Empty Quadrant: AI Teammates for Embodied Field Learning

For four decades, AIED research has rested on what we term the Sedentary Assumption: the unexamined design commitment to a stationary learner seated before a screen. Mobile learning and museum guides have moved learners into physical space, and context-aware systems have delivered location-triggered content -- yet these efforts predominantly cast AI in the role of information-de-livery tool rather than epistemic partner. We map this gap through a 2 x 2 matrix (AI Role x Learning Environment) and identify an undertheorized intersection: the configuration in which AI serves as an epistemic teammate during unstruc-tured, place-bound field inquiry and learning is assessed through trajectory rather than product. To fill it, we propose Field Atlas, a framework grounded in embod-ied, embedded, enactive, and extended (4E) cognition, active inference, and dual coding theory that shifts AIED's guiding metaphor from instruction to sensemak-ing. The architecture pairs volitional photography with immediate voice reflec-tion, constrains AI to Socratic provocation rather than answer delivery, and ap-plies Epistemic Trajectory Modeling (ETM) to represent field learning as a con-tinuous trajectory through conjoined physical-epistemic space. We demonstrate the framework through a museum scenario and argue that the resulting trajecto-ries -- bound to a specific body, place, and time -- constitute process-based evi-dence structurally resistant to AI fabrication, offering a new assessment paradigm and reorienting AIED toward embodied, dialogic human-AI sensemaking in the wild.

Hyein Kim, Sung Park

School of Data Science and Artificial Intelligence, Taejae University

AI in EducationHuman-AI CollaborationEmbodied AI
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Mar 4, 2026

STEM Faculty Perspectives on Generative AI in Higher Education

Generative artificial intelligence (GenAI) tools are increasingly present in higher education, yet their adoption has been largely student-driven, requiring instructors to respond to technologies already embedded in classroom practices. While some faculty have embraced GenAI for pedagogical purposes such as content generation, assessment support, and curriculum design, others approach these tools with caution, citing concerns about student learning, assessment validity, and academic integrity. Understanding faculty perspectives is therefore essential for informing effective pedagogical strategies and institutional policies. In this paper, we present findings from a focus group study with 29 STEM faculty members at a large public university in the United States. We examine how faculty integrate GenAI into their courses, the benefits and challenges they perceive for student learning, and the institutional support they identify as necessary for effective and responsible adoption. Our findings highlight key patterns in how STEM faculty engage with GenAI, reflecting both active adoption and cautious use. Faculty described a range of pedagogical applications alongside concerns about student learning, assessment, and academic integrity. Overall, the results suggest that effective integration of GenAI in higher education requires rethinking assessment, pedagogy, and institutional governance in addition to technical adoption.

Akila de Silva, Isabel Hyo Jung Song, Hui Yang +1

Department of Computer Science, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132 USA

Higher EducationGenerative AI in EducationAI in Education
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Mar 4, 2026

A debate game about societal impacts of Artificial Intelligence

Artificial intelligence (AI) is now ubiquitous in our lives, and we regularly experience its decisions. Yet, the general public has very little knowledge about how it works, its use of data, its lack of objectivity, and its fallibility. In line with UNESCO recommendations, we believe that a basic understanding of AI algorithms is essential to enable everyone to make informed choices about using them and entrusting them with personal data. To this end, we propose a debate game simulating a municipal council gathered to choose between three AI solutions proposed by a company to address a societal problem. This interactive tool is available for free download online. The results of the initial sessions demonstrate its value, not only for raising awareness about AI but also for developing argumentation and listening skills in students.

Carole Adam, Cedric Lauradoux

MCF, LIG, Univ. Grenoble-Alpes · Univ. Grenoble-Alpes, INRIA Privatics

AI LiteracyEducational TechnologyAI in Education
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