AI Powered Education
Education, as a vital institution, continuously benefits modernization, with artificial intelligence (AI) driving a paradigm shift in teaching and learning. This research presents an AI-powered e-learning system that delivers personalized academic notes tailored to students' needs. Utilizing a Large Language Model (LLM) like Gemini, the system comprehends student queries more effectively, enabling customization of content across diverse subjects and academic levels. Prompt engineering ensures that information remains accessible, accurate, suitable for different age groups. Key features of the system include an AI-based chatbot interface developed using HTML, CSS, and JavaScript, providing user-friendly experience. The back-end integrates with the LLM via an API to process student inputs and generate relevant information in real time. Additionally, light and dark modes usability, making the platform adaptable to user preferences. It also outlines the system’s design, data preparation, prompt engineering methods, and integration of the front-end and back-end. It highlights key challenges, such ensuring response clarity, maintaining student engagement, and managing different learning speeds. Initial testing, with student feedback, revealed high response accuracy, usefulness, and user satisfaction, underscoring AI's potential in enhancing learning quality and engagement. With its adaptability to multiple subjects, the system is positioned as a crucial tool in modern education. This project demonstrates how AI can transform established learning paradigms, delivering personalized education and significantly enhancing learning experiences.
Authors
- Sinchan Mallick
Institutions
- University of Engineering & Management (IN)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- International Journal for Research in Applied Science and Engineering Technology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.22214/ijraset.2025.70678
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00