The increasing academic workload, diverse learning needs, and mental stress faced by college students have created a demand for smart study planning systems that fit individual learning styles. Traditional planners often create fixed schedules and ignore a student's emotional state, learning progress, or personal preferences. This oversight can lower learning efficiency and motivation. This research introduces an AI-Powered Emotion-Aware Study Planner for College Students. It combines artificial intelligence (AI), emotion recognition, and personalized recommendation methods to develop flexible study schedules. The system evaluates students' emotional states using facial expressions or text-based sentiment analysis and considers their academic performance, study history, subject difficulty, and available study time. With this information, the AI recommendation engine creates customized study plans. It ranks subjects, suggests suitable break times, and provides motivational feedback to improve learning engagement. The system monitors students' progress and modifies future study schedules based on changes in emotional state or academic performance. To assess its effectiveness, the research uses machine learning algorithms like Random Forest and Support Vector Machine to classify emotional states and create personalized recommendations. This framework aims to enhance study effectiveness, reduce academic stress, increase motivation, and improve overall academic performance. This intelligent approach can serve as a valuable educational support tool for higher education institutions by promoting flexible, student-centered, and emotionally aware learning environments.

Keywords:

Artificial Intelligence, Emotion Recognition, Personalized Learning, Study Planner, Machine Learning, Sentiment Analysis, Academic Performance, Student Well-being, Adaptive Learning.