Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

AI- Powered Student Assistance ChatBot

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  • Shahrukh Saifi,
  • Adeeb Pasha K A,
  • Shobha J,
  • Sneha S,
  • Jyoti Kiran M
Shahrukh Saifi
School of Computer Science and Engineering, REVA University, Bengaluru, India
Adeeb Pasha K A
School of Computer Science and Engineering, REVA University, Bengaluru, India
Shobha J
School of Computer Science and Engineering, REVA University, Bengaluru, India
Sneha S
School of Computer Science and Engineering, REVA University, Bengaluru, India
Jyoti Kiran M
School of Computer Science and Engineering, REVA University, Bengaluru, India
DOI: https://doi.org/10.5281/zenodo.15483879

Published 2025-05-21

Keywords

  • Natural Language Processing,
  • AI,
  • Chatbot,
  • Student Services,
  • Voice Interaction,
  • Multilingual Support
    • ...More
    Less

How to Cite

Shahrukh Saifi, Adeeb Pasha K A, Shobha J, Sneha S, & Jyoti Kiran M. (2025). AI- Powered Student Assistance ChatBot. International Journal of Computational Learning & Intelligence, 4(4), 822–831. https://doi.org/10.5281/zenodo.15483879

Copyright (c) 2025 Shahrukh Saifi, Adeeb Pasha K A, Shobha J, Sneha S, Jyoti Kiran M

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

In the rapidly evolving digital education landscape, students require instant, accurate, and accessible academic and administrative support. This paper presents the design and implementation of an AI-powered Student Assistance Chatbot, tailored to serve institutions like REVA University and the Department of Technical Education, Government of Rajasthan. The chatbot leverages Natural Language Processing (NLP), a custom-trained dataset, and live data scraping to address queries related to admissions, fees, scholarships, placements, and more, across English and regional languages. The system aims to reduce dependency on human staff, ensure 24/7 support, and provide scalable automation. The implementation results indicate a high accuracy in intent recognition, quick response generation, and improved student satisfaction

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