Educational institutions often face challenges in providing timely, accurate, and personalized academic assistance due to the absence of centralized and intelligent support systems. This paper presents INDRA, an integrated agentic artificial intelligence–based academic assistance framework designed to enhance student learning experiences in technical education environments. The system leverages a multi-agent architecture combining large language models with task-specific modules to deliver functionalities such as syllabus-aware tutoring, automated study planning, attendance tracking, and real-time academic analytics. By incorporating context-aware filtering based on department and semester, the system ensures that all generated responses remain relevant, structured, and aligned with institutional curricula. The framework utilizes a modular pipeline consisting of data processing, agent orchestration, and user interaction layers, enabling scalable and efficient deployment. Experimental evaluation demonstrates improvements in information accessibility, reduction in manual effort, and enhanced academic decision-making support. The system maintains low response latency while ensuring high usability and adaptability across different academic domains. Overall, the proposed approach provides a practical and intelligent solution for bridging the gap between students and academic resources, contributing to improved learning efficiency, engagement, and institutional productivity.
Sakore et al. (Thu,) studied this question.