An Explainable AI-Based Adaptive Study Planner for Personalized and Stress-Aware Learning

Planning is essential. Students often know what to study but not how to study and when to study, due to increasing academic workload, stress and lack of personalized study planning tools.Existing study planners often generate static schedules These fail to adapt to students changing priorities , productivity levels and mental wellbeing of the student. In this paper we present an AI-based adaptive study planner that generates personalized study schedules based on user preferences, deadlines and stress level .The proposed system integrates adaptive scheduling techniques, pomodoro-based time management, and a feedback – driven learning mechanism to continually improve scheduling recommendations. An explainable AI layer is added to provide explanations of schedule and recommend related real time course . The system takes inputs from the user including tasks,available time , and stress levels to dynamically adjust study plans and promote well being balanced learning.The proposed approach aims to enhance productivity , reduce stress , and improve efficiency by giving intelligent and flexible scheduling . The results depict improved task consistency , workload balance and adaptive personalization compared to conventional static study planning tools used by students.