Student Learning Using AI

Artificial intelligence (AI) is increasingly being explored as a way to make digital learning more responsive to students’ individualneeds. This paper proposes an AI-assisted student learning system that combines learner preferences, topic-level progress, practiceresponses, and teacher feedback to recommend suitable learning resources and next-step activities. The proposed framework includesa learner interface, a content repository, a recommendation and feedback engine, progress analytics, and teacher oversight. Ratherthan treating a single score as a complete picture of ability, the system uses repeated learning evidence to identify topics that mayneed revision and to suggest practice at an appropriate level. The design emphasizes explainable recommendations, privacy,accessibility, and human supervision. A prototype could be developed using a web interface, a structured content store, and rulebased or machine-learning components selected according to available data. Evaluation should measure recommendation relevance,consistency, usability, learner engagement, and learning outcomes through a controlled and ethically approved study. Noexperimental performance is claimed in this conceptual paper; the framework and evaluation plan are presented for futureimplementation.