Personalized Study Assistant and Smart Learning Recommendations: An Individual Subsystem of the Intelligent Digital Library with Personalized Study Assistance

Digital libraries give students access to books, lecture notes and research material, but they are designed mainly for storage and retrieval. They offer little help in understanding concepts, seeing how concepts depend on one another, or deciding what to study next. This project designed, implemented and tested a prototype Intelligent Digital Library with Personalized Study Assistant and Smart Learning Recommendations to address this gap. The system represents each topic as a PCSMR record: the Problem a concept addresses, the Challenge that made the problem difficult, the Solution proposed, the Mechanism by which it works, and the Result it achieves. A language model drafts these records from uploaded PDF lecture material, and the learner reviews and edits each draft before relying on it. Concepts are linked by prerequisite relationships, with cycle prevention and a prerequisite-first learning path. Each concept can be viewed as notes, a flow diagram and an interactive concept map, and generated multiple-choice quizzes update a per-learner mastery record. The prototype was built with React, FastAPI and PostgreSQL. All AI access is isolated behind a replaceable provider interface, so the system keeps working when the AI provider changes. Four automated backend tests covering nine test cases were recorded as passing, and a live extraction check on a sample passage produced a coherent draft in which only some fields could be traced to the source text. These tests show that the workflows behave as designed. They do not measure extraction accuracy, quiz quality, usability or learning gain, so the report defines an evaluation method for each. Feedback on the prototype identified the need for adaptive, multi-mode assessment with remediation, fuller study notes and an improved interface, and these are specified as recommendations. The study shows that structured, reviewable concept records can serve as a single foundation for several learning presentations and for prerequisite-based study guidance.