A PRIORITY-BASED PERSONALIZED STUDY SCHEDULING SYSTEM

Students must balance lectures, assignments, practical work, revision, examinations, and personal responsibilities. When many tasks compete for limited time, learners may spend too long on easy activities, postpone important work, or underestimate the effort required for difficult subjects. A structured schedule can help, but a rigid timetable may become ineffective when deadlines change or a planned session is missed. A personalized scheduling system can support planning by considering both task priority and the student’s real availability. Instead of treating every task equally, it can rank activities according to urgency, importance, estimated duration, difficulty, and progress. The schedule should remain understandable and adjustable, so the student can correct priorities and avoid an unrealistic workload. j p A. Problem Statement Conventional study planning often depends on manual lists and fixed timetables. These methods may not adapt well to changing deadlines, varying task difficulty, or unexpected interruptions. The problem addressed in this paper is to design a system that ranks academic tasks transparently and generates an achievable personalized schedule that can be updated as the learner progresses. B. Aim and Objectives The objectives are to collect tasks and deadlines; calculate a priority score using clear criteria; allocate available study time across subjects; provide reminders and progress tracking; revise the schedule when tasks are completed or delayed; and assess the usefulness, feasibility, and usability of the generated plans. C. Scope and Contribution The proposed system is intended for students managing multiple subjects and academic commitments. Core functions include task entry, priority ranking, availability management, calendar-based scheduling, reminders, and progress summaries. The system is a planning aid rather than a replacement for a teacher’s guidance or the student’s judgment. Its main contribution is a transparent connection between task priority and time allocation.

Fig. 2. Task ranking and schedule generation workflow. C. Handling Conflicts and Missed Sessions If the available study time is insufficient for all tasks, the system should identify the conflict instead of silently creating an impossible plan. It can suggest moving lower-priority tasks, dividing a long task into shorter sessions, or asking the student to revise availability. When a session is missed, the remaining tasks should be re-ranked using current deadlines and progress. The student should be able to accept, modify, or reject proposed changes.

A. Limitations Estimated task duration and difficulty are subjective and may change as learning progresses. Students may not enter every commitment, and an unexpected event can invalidate a schedule. A weighted score cannot fully capture every academic context. The proposed scoring formula is a starting point that requires testing and adjustment rather than a universally optimal rule. B. Responsible Use The system should support learners without encouraging excessive study hours or treating productivity scores as a measure of personal worth. Breaks, sleep, accessibility needs, and non-academic responsibilities should be respected. Student data should not be used to make high-stakes academic judgments without consent, validation, and appropriate human oversight. C. Future Scope Future versions may include calendar integration, spaced-repetition reminders, exam-date planning, collaborative study groups, and explainable recommendations based on completion history. A larger evaluation could compare alternative scoring weights, investigate how duration estimates improve over time, and examine whether the system helps students maintain realistic plans across different courses.

This paper proposes a priority-based personalized study scheduling system that combines task ranking, availability-aware time allocation, reminders, and progress tracking. The framework considers urgency, importance, difficulty, effort, and learner-specific relevance while maintaining a feasible schedule and allowing user adjustments. The paper presents a modular architecture, illustrative scoring method, scheduling workflow, implementation plan, and evaluation metrics. Future implementation should validate schedule feasibility, usability, privacy, and practical benefit before making claims about improved academic outcomes.