Intelligent Tutoring Systems and Adaptive Learning

Study examines how explainable intelligent assessment platform supports assessment literacy development in pre-service teachers through visualized reasoning and evidence-based reflection.

Fine-tuned language model provides automated question answering in large calculus courses, achieving 75% accuracy. Hybrid human-AI workflows maintain instructor oversight while scaling support.

Study of 107 K-3 teachers reveals 80% use AI for instructional materials but express concerns about student writing tasks due to developmental readiness and creativity loss.

Semester-long trial comparing directive, metacognitive, and hybrid AI-generated feedback on 329 students' revision behavior, confidence, and work quality in design and programming.

Study evaluates generative AI's capacity to assess and provide feedback on elementary students' epistemic understanding in science inquiry, demonstrating high reliability with human raters.

AlphaLearn proposes a multi-objective evolutionary framework for adaptive e-learning pathways that integrates fairness as a core optimization criterion alongside learning effectiveness and engagement.

Hybrid framework combining graph networks and transformers with synthetic data augmentation for automatic student engagement recognition from facial video in e-learning systems.

How educators reimagine course objectives to emphasize human cognition, critical judgment, and creativity in response to generative AI capabilities.

University course integrating AI/ML software development with educational research training, producing tools for K-12 instruction through authentic school partnerships.

Formative adaptive assessment framework combining Item Response Theory-based computerized adaptive testing with Bayesian diagnostic modelling for engineering education competency assessment.

Doctoral research examining how student prompting behaviours with Large Language Models correlate with learning outcomes in programming, developing behavioural models and adaptive feedback systems.

Gurukulya is an AI-powered smart campus platform combining LLMs, sentiment analysis, and automated assessment to boost student engagement, reduce evaluation time by 85%, and improve learning.

Study reveals Grade 11 students struggle with fundamental programming concepts like variables and loops before formal instruction, suggesting need for scaffolded teaching approaches.

TC-MAG framework uses multi-agent AI to improve automated grading of open-ended student responses with explainable partial credit assessment and teacher oversight capabilities.

AI-assisted learnersourcing framework balancing student learning and content reusability in database programming education through structured collaborative roles between learners and LLMs.

Automated portfolio system transforms educational assessment aligned with NEP 2020, improving waste management knowledge retention among women through technology-enabled learning progression tracking.

Systematic survey of 1,125 papers from flagship conferences identifying 172 open datasets in learning analytics, with PRACTICE guidelines for dataset publication and reproducibility practices.

Methodological demonstration of CTT and IRT operationalization in spreadsheet software for classroom assessment analysis and item bank construction using simulated datasets.

Study examines predictive validity of computer-based formative assessment for standardised test outcomes, finding models explain 30-48% of variance with domain-specific patterns.