Professor Nova: An Intellengent Personalized Learning Guide Using Generative AI

Traditional e-learning systems struggle to deliver truly personalized educational experiences due to static content delivery, lack of real-time assessment, and absence of adap- tive feedback mechanisms. This paper presents Professor Nova, an intellgent GenAI-powered personalized learning guide that leverages Large Language Models (LLMs) via the Groq Infer- ence engine [9], Langchain orchestration [7], and LangGraph state management [8]to deliver adaptive teaching, automatic quiz generation, weakness analysis, and individualized progress tracking. The system implements a chat-based conversational teaching paradigm where students interact freely with an AI teacher agent. Upon topic selection, Professor Nova generates structured explanations followed by automatically generated five- question multiple-Choice Quizzes (MCQs). Student responses are evaluated to compute a score (0-100), identify weak concepts, de- liver targeted re-teaching using alternative analogies, and persist performance data to a SQLite database for longitudinal progress tracking. User authentication, session management, and a mod- ern Streamlit-based interface with a three-panel sidebar(Profile, History, Settings) complete the system [10]. Experimental results demonstrate that the system effectively identifies student weak- nesses, adapts instructional content accordingly, and maintains comprehensive learning histories. The proposed architecture provides a scalable, cost-effective solution for personalized e- learning accessible to students of all levels.