Smart Study Assistant using AI for Personalized Learning

The rapid advancement of Artificial Intelligence has significantly transformed modern educational methodologies, enabling the development of intelligent systems that enhance learning efficiency and accessibility. This paper presents the implementation and evaluation of an AI-powered Smart Study Assistance System designed to automate key academic tasks and improve conceptual understanding. Building upon the architectural framework proposed in Stage 1, the system integrates Generative Artificial Intelligence and Natural Language Processing techniques to provide functionalities such as automated text summarization, dynamic multiple-choice question (MCQ) generation, and analogy-based explanation. The system is implemented as a web-based application using React for the frontend interface, Flask for backend processing, and MongoDB for data storage, while AI-driven content generation is achieved through GPT-based models accessed via APIs. The proposed system processes user-provided academic content and generates structured outputs in real time, thereby reducing manual effort and enhancing learning productivity. Experimental evaluation is conducted using diverse academic inputs to assess system performance in terms of response time, output quality, and feature effectiveness. The results demonstrate that the system is capable of generating concise and contextually relevant summaries, meaningful self-assessment questions, and simplified explanations for complex topics. Although minor variations in response time are observed for larger inputs due to AI processing constraints, the system maintains overall efficiency and usability. The findings of this study validate the effectiveness of integrating multiple AI-driven functionalities into a unified platform for intelligent learning assistance. The proposed system not only improves study efficiency but also supports self-directed learning and conceptual clarity, making it a scalable and practical solution for modern educational environments