Explainable AI-Driven Student Success Platform for Personalized Learning Well-Being and Academic Planning in Schools

Abstract

This paper presents a detailed explainable AI-driven student success platform designed for school education. The platform goes beyond a narrow marks-only view by integrating academic performance, sleep cycle, study time, extracurricular balance, task completion, and long-term goal alignment into a single advisory system. A Python Flask backend receives student inputs, a JavaScript and HTML interface presents interactive analytics, and the Grok AI API is used as an AI mentor layer that converts structured signals into understandable guidance, resources, and action plans. The project was tested as a pilot-scale experimental validation in a Class 11 setting with 30 students. Instead of a traditional lecture-only routine, the intervention used AI-assisted and game-based learning. For example, programming concepts such as functions were explained with Minecraft-style logic in Code.org, while short task-based activities were used to reduce cognitive overload and increase participation. For privacy-preserving reporting, this paper presents an anonymized score slice from the intervention and summarizes the improvement using descriptive statistics. The mean score of the reported slice increased from 31.9 to 47.1 out of 70, showing a 48% relative gain over the baseline. The paper contributes a system architecture, a student success index, a practical deployment pattern, and a classroom-validation story that combines academic guidance with lifestyle-aware support. The resulting manuscript is intended to be strong enough for an interdisciplinary applied journal where systems, analytics, and educational technology are evaluated together.