AN INTELLIGENT PERSONAL ASSISTANT FOR PERSONALIZED EDUCATIONAL VIDEO RECOMMENDATION

Abstract

This project presents the design and development of an intelligent personal assistant for personalized educational video recommendation, aimed at improving the efficiency and effectiveness of online learning. With the rapid growth of digital educational content, learners often face challenges in identifying relevant resources and organizing structured learning paths. The proposed system addresses this issue by integrating artificial intelligence, machine learning, and natural language processing techniques to deliver customized learning experiences. The system allows users to input their learning goals, based on which it automatically generates a structured, daywise learning plan. It further enhances learning by selecting the most relevant educational videos, creating organized playlists, and generating well-formatted documents containing the complete learning roadmap. The use of AI agents, Model Context Protocol (MCP), and external tools such as YouTube, Google Drive, and Notion enables seamless automation of multi-step workflows. Additionally, the system emphasizes user interaction, transparency, and personalization through an intuitive Streamlit-based interface. It ensures that recommendations are context-aware, relevant, and aligned with user preferences. Experimental results demonstrate that the system is reliable, efficient, and capable of handling diverse learning goals while reducing manual effort.