Intelligent video learning assistant using LLaMA model

This paper presents an intelligent video learning assistant that leverages advances in artificial intelligence and natural language processing to enhance learning from video-based educational content. The proposed system automatically extracts transcripts from online videos, generates concise summaries, and creates contextually relevant quiz questions using the LLaMA-3 large language model. By integrating transcript summarization, automated question generation, and semantic answer evaluation, the system transforms passive video consumption into an interactive learning experience. The architecture employs FastAPI for backend processing, React.js for a responsive user interface, and Fireworks AI for efficient model inference. Experimental observations demonstrate that the system reduces the time required to extract key concepts from long videos while improving learner engagement through immediate feedback and self-assessment. The proposed solution is particularly beneficial for students, educators, and professionals seeking an efficient and structured approach to video-based learning.