Harnessing Machine Learning for Intelligent Educational Tools and Learning Analytics

Dear Colleagues,

Higher education is rapidly adopting AI-enabled systems across assessment, feedback, learning support and institutional analytics. At the same time, universities face growing pressures for scalable, timely and equitable learning support, alongside heightened scrutiny of academic integrity, transparency and data governance. Recent advances in machine learning, including large language models, multimodal learning and privacy-preserving methods, create new opportunities to design intelligent educational tools that are both effective and responsibly deployable. However, translating these capabilities into robust AI systems for authentic higher education contexts remain challenging due to domain shift across courses and cohorts, high-stakes decision consequences and the need for trustworthy evaluation.

This Special Issue aims to curate rigorous contributions on machine learning as an enabling foundation for intelligent educational tools and learning analytics, with an emphasis on AI systems that are theoretically grounded, technically reproducible and validated in higher education settings. The Special Issue aligns with the journal AI section AI Systems: Theory and Applications by focusing on system design, implementation, evaluation protocols and deployment considerations that advance the state of practice.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Learner modelling and knowledge tracing, including uncertainty-aware and robust approaches;

Learning analytics pipelines for prediction and intervention, including causal and counterfactual methods;

Intelligent tutoring and feedback systems using ML, including retrieval-augmented and constrained generation;
Automated formative assessment and rubric-aligned feedback for writing, programming and project work;

Explainable and interpretable learning analytics for educator decision-making;

Fairness, bias auditing and measurement validity in educational ML systems;

Privacy-preserving learning analytics (federated learning, differential privacy, secure computation) ;

Academic integrity analytics and integrity-aware assessment system design;

Multimodal analytics (text, clickstream, audio/video) and calibration for educational use;

Reproducible evaluation methods, benchmarks and MLOps for educational AI systems (drift monitoring, lifecycle governance).

I look forward to receiving your contributions.

Dr. Jason Zagami
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• learning analytics
• intelligent tutoring systems
• automated feedback
• higher education assessment
• explainable AI
• fairness
• privacy
• multimodal analytics
• academic integrity