Volume 2 - Issue 2, March - April 2026

This paper presents the design of an AI-Driven Study Habits Reinforcement System (AI-SHRS) which aims to improve student productivity through adaptive monitoring, behavior analysis and reinforcement-based prompts. The web application applies machine learning models to evaluate their study activity (time invested, completion rates and focusing time) to suggest tailored recommendations for developing personal motivation towards study activities. It was designed with PHP, MySQL, Python and Bootstrap incorporating AI logistics in real-time using reinforcement learning and predictive analytics. An evaluation with 35 participants (IT students and staff) resulted in an average SUS rating of 86.8 (Excellent). The system achieved high accuracy (91.4%) as well as efficiency on processing the logs (less than 1 second). The valuable influence of the system to motivate, self-manage and productivity awareness was stressed by qualitative feedback. The current results support the proposition that AI-driven reinforcement dynamics can indeed enhance learning habits and maximize academic achievement in online learning platforms.

Reginald S. Prudente, Reynaldo B. Consad, Jobert C. Gigante, Floyd C. Mecator,"Development of an AI-Driven Study Habit Reinforcement System on Student Productivity" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(1622-1634) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.