Across health professions education, big data and learning analytics (LA) are moving from pilots to institution-wide services that personalize learning, surface risk early, and strengthen assessment. Recent reviews in medical education highlight four effects with the most consistent evidence:

Personalized pathways & timely feedback by mining LMS traces, simulation logs, and e-portfolios to recommend next actions and resources.

Early-warning systems that flag learners likely to struggle—useful when triangulated with OSCE, EPA, and workplace data rather than a single data source.

Assessment quality & scalability, e.g., dashboarding item performance and supporting programmatic assessment with explainable AI-assisted judgments.

Curriculum intelligence & equity monitoring, mapping curricular exposure and detecting opportunity gaps across learner subgroups.

At the same time, rigorous syntheses caution that measured outcome gains (knowledge, skills, behaviors) from AI/LA interventions are still uneven—calling for stronger study designs and clearer evaluation criteria.

Implementation Essentials

Start with a small, theory-informed pilot with pre-specified success metrics, then scale.

Use multisource data (LMS + OSCE/EPA + simulation) and validate each source’s contribution to predictions.

Embed governance, privacy, and model transparency (consent, data minimization, explainability, and appeal). AMEE’s recent guide on AI in assessment is a practical anchor for policy and faculty.

This video demonstrates how data analytics can be applied to personalize medical students’ learning pathways, predict performance, and provide targeted feedback to enhance academic success.
Link: https://www.youtube.com/watch?v=kZ5c74SfEuU

Read (Open Access)
Exploring the Transformative Potential of Learning Analytics in Medical Education: A Systematic Review (2025)—an up-to-date synthesis of LA applications, benefits, and challenges tailored to medical education contexts.
Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC11788773/