An AI-Based Smart Study Planner for University Class Scheduling Optimization
DOI:
https://doi.org/10.11113/ijic.v16n1-2.692Keywords:
Artificial Intelligence (AI), Smart Study Planner, Class Scheduling Optimization, Academic Planning Systems, CGPA Prediction, Predictive Analytics in Education, AI Chatbot Advisory, Laravel Framework, Educational Decision Support SystemsAbstract
Efficient course scheduling remains a major challenge in higher education, where manual registration and limited predictive tools often delay student progression and strain institutional resources. This study presents an AI-based Smart Study Planner that integrates predictive analytics, real-time student input, and an AI-driven chatbot advisory system to optimize academic scheduling. The planner forecasts cumulative grade point average (CGPA), automates course recommendations, and provides administrators with real-time demand analytics for resource allocation. Developed using the Laravel framework with MySQL and ApexCharts, the system was validated through User Acceptance Testing (UAT) involving 10 undergraduate students and 3 academic administrators. Quantitative evaluation indicated a 29% improvement in planning efficiency, 25% reduction in registration conflicts, and overall user satisfaction of 4.6/5, while benchmarking against Coursicle, CGPA Forecaster, and Ellucian Degree Works confirmed higher predictive accuracy (91%) and usability. These findings demonstrate that the proposed planner is a cost-effective, scalable, and empirically validated solution that enhances student decision-making and administrative planning. The research contributes both a practical implementation framework and empirical evidence supporting AI-driven optimization as a viable approach to addressing persistent inefficiencies in academic scheduling. Overall, the findings demonstrate that the AI-based smart study planner is cost-effective, scalable, and user-friendly, addressing critical shortcomings in existing solutions.
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