AI Chatbot for Syllabus-Based Academic Support

The evolution of digital education has necessitated intelligent academic support systems to assist students with syllabus-based learning and academic planning. This paper presents an innovative solution — an AI Chatbot for Syllabus-Based Academic Support — designed to provide real-time assistance on academic queries, exam schedules, and assignment deadlines. The proposed system integrates a lightweight local Large Language Model (LLM), TinyLlama-1.1B-Chat, ensuring fast and resource-efficient response generation. The chatbot architecture combines a Flutter frontend for mobile accessibility, a FastAPI backend for processing and orchestration, and Firebase Firestore for secure and scalable academic data storage. Students can interact conversationally with the chatbot to obtain syllabus topics, exam dates, and assignment deadlines, with historical chats and personalized feedback mechanisms also incorporated. Unlike conventional heavy LLM-based systems, this implementation leverages TinyLlama to deliver efficient and accurate support without high computational demands, making it ideal for mobile-first academic environments. Experimental results show significant improvements in response speed and usability. This project demonstrates the potential of lightweight AI models in transforming educational support platforms for enhanced student learning experiences.