An Integrated Agentic AI Framework For Personalized Academic Assistance In Technical Education_819

This paper introduces an integrated agentic framework leveraging generative artificial intelligence (AI) to provide comprehensive academic assistance in technical education. The system utilizes a multi-agent architecture powered by large language models (LLMs) and retrieval-augmented generation (RAG) to deliver personalized support including syllabus-aware tutoring, intelligent study planning, and real-time attendance analytics. By grounding generative responses in department-specific datasets (CSE, AI-DS, ECE), the framework mitigates hallucinations and ensures curriculum alignment. Experimental validation with 150 students across three engineering departments demonstrates an 87% satisfaction rate, a 65% reduction in information-seeking time, and a 42% improvement in study plan adherence. The results suggest that agentic workflows can significantly enhance student engagement and administrative efficiency in higher education environments.