Adaptive AI-Driven Learning Systems for Personalized Student Engagement and Performance

Abstract

Adaptive AI-driven learning systems personalize instruction by estimating learner state and dynamically selecting content, feedback, and pacing to improve mastery and engagement. This paper synthesizes peer-reviewed evidence on adaptive learning, intelligent tutoring, knowledge tracing, educational data mining, and recommender systems, and proposes an applied engineering framework suitable for deployment in higher-education STEM contexts. We ground personalization in classic student modeling (knowledge tracing) and modern sequence modeling (deep knowledge tracing), and integrate a multidimensional view of engagement to avoid reducing “engagement” to simple clickstream metrics. We then present a modular, service-oriented system architecture encompassing data ingestion, learner modeling, pedagogical decisioning, explainability, monitoring, and governance controls. A prototype evaluation is conducted using a simulation-based testbed (explicitly illustrative, not empirical) with synthetic learners and skills. Across 600 simulated learners and 25 skills over 120 learning steps, an adaptive policy improves average mastery (fraction of skills mastered at threshold) compared to non-adaptive paging and random sequencing, with markedly higher rates of reaching “80% mastery.” The results also show that naive optimization may widen outcome gaps across learner subgroups, motivating fairness-aware objectives and human-in-the-loop controls. Ethical, privacy, and accessibility requirements are addressed through risk management practices, differential privacy–compatible training options, transparent explanations, and WCAG-aligned interface design.