Volume 15, Issue 8 · IJARST · ISSN 2319-1783 / 2320-1126

Design and Development of an AI-Powered Intelligent Chatbot for Student Information Services in Higher Educational Institutions Using Large Language Models

Design and Development of an AI-Powered Intelligent Chatbot for Student Information Services in Higher Educational Institutions Using Large Language Models is an open-access, peer-reviewed research paper by OHIA Paul Ndudiri and OLOJIDO Joseph Bamikole, published in Volume 15, Issue 8 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

OHIA Paul Ndudiri and OLOJIDO Joseph Bamikole

Abstract

Higher educational institutions increasingly face pressure to respond quickly and consistently to routine student enquiries on admissions, registration, examinations, course requirements, and institutional policies. This study designed, developed, and evaluated an AI-powered chatbot for student information services using a Retrieval-Augmented Generation (RAG) architecture. Institutional documents, including the student handbook, admission guidelines, frequently asked questions, and course catalogue, were pre-processed into retrievable knowledge chunks and connected to a Large Language Model through a Flask-based backend and browser-based chat interface. A formative pilot evaluation was conducted with 30 volunteer participants, comprising 24 undergraduate students and 6 administrative or academic-affairs staff. Across 120 test queries, 94 responses were rated fully correct, 19 partially correct, and 7 incorrect or irrelevant, giving a combined fully/substantially correct response rate of 94.2%. The chatbot returned responses in an average of 3.8 seconds under normal network conditions, while 82.0% of satisfaction ratings were 4 or 5 on a five-point Likert scale. Only 9 of the 120 queries, representing 7.5%, required fallback or escalation to human staff because they were ambiguous, sensitive, or outside the available knowledge base. The findings indicate that combining Large Language Models with RAG-grounded institutional content can improve the availability, responsiveness, and consistency of routine student information services in Nigerian polytechnic and university contexts, provided the knowledge base is regularly updated and human escalation remains available for complex cases.

Keywords: Artificial Intelligence Chatbot; Large Language Models; Retrieval-Augmented Generation; Student Information Services; Higher Education; Natural Language Processing; Conversational Agents

DOI: https://doi.org/10.62226/ijarst20262787

References

Aguila, A., Ngoc, N. T., Nguyen, N. A. D., Huynh, K.-T., Mai, A., Le, T. D., & Tuyen, N. T. V. (2024). Large language model in higher education: Leveraging Llama2 for effective academic advising. In 2024 International Conference on Advanced Technologies for Communications (ATC) (pp. 590-595). IEEE. https://doi.org/10.1109/ATC63255.2024.10908321

Aloqayli, A., & Abdelhafez, H. (2023). Intelligent chatbot for admission in higher education. International Journal of Information and Education Technology, 13(9), 1348-1357. https://doi.org/10.18178/ijiet.2023.13.9.1937

Shosanya, S. O., Olunrebi, A. A., Kareem, M. A., Akinwande, D. O., & Babatunde, M. A. (2025). Design and implementation of an AI chatbot for prospective student support. Journal of Science and Information Technology, 19(2), 159-174.

EdTech Magazine. (2025, May 17). How three universities developed their chatbots. https://edtechmagazine.com/higher/article/2025/05/how-three-universities-developed-their-chatbots

Endurance, A., Onah, E. N., Uzoma, A. C., Andor, S. E., & Orisakwe, C. U. (2021). Integration of artificial intelligence tool (AI-chatbot) into teaching and learning: A panacea for improving universities educational and administrative duties in South-South, Nigeria. Journal of Computer Science & Systems Biology, 14, 357.

Es, S., James, J., Espinosa-Anke, L., & Schockaert, S. (2023). RAGAS: Automated evaluation of retrieval augmented generation. arXiv. https://arxiv.org/abs/2309.15217

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248, 1-38. https://doi.org/10.1145/3571730

Kooli, C. (2023). Chatbots in education and research: A critical examination of ethical implications and solutions. Sustainability, 15(7), Article 5614. https://doi.org/10.3390/su15075614

Kuhail, M. A., Alturki, N., Alramlawi, S., & Alhejori, K. (2023). Interacting with educational chatbots: A systematic review. Education and Information Technologies, 28(1), 973-1018. https://doi.org/10.1007/s10639-022-11177-3

Kumar, J. A. (2021). Educational chatbots for project-based learning: Investigating learning outcomes for a team-based design course. International Journal of Educational Technology in Higher Education, 18(1), Article 65, 1-28. https://doi.org/10.1186/s41239-021-00302-w

Ndunagu, J., Jimoh, R., Chidiebere, U., & Opeoluwa, G. (2022). Enhanced open and distance learning using an artificial intelligence (AI)-powered chatbot: A conceptual framework. In Proceedings of the 2022 5th Information Technology for Education and Development (ITED). IEEE.

Ndunagu, J. N., Ezeanya, C. U., Onuorah, B. O., Onyeakazi, J. C., & Ukwandu, E. (2025). A chatbot student support system in open and distance learning institutions. Computers, 14(3), Article 96. https://doi.org/10.3390/computers14030096

Nguyen, M.-T., Tran-Tien, M., Viet, A. P., Vu, H.-T., & Nguyen, V.-H. (2021). Building a chatbot for supporting the admission of universities. In 2021 13th International Conference on Knowledge and Systems Engineering (KSE) (pp. 1-6). IEEE.

Nguyen, T.-H., Tran, D.-N., Vo, D.-L., Mai, V.-H., & Dao, X.-Q. (2022). AI-powered university: Design and deployment of robot assistant for smart universities. Journal of Advances in Information Technology, 13(1), 78-84. https://doi.org/10.12720/jait.13.1.78-84

Nguyen, T. T., Le, A. D., Hoang, H. T., & Nguyen, T. (2021). NEU-chatbot: Chatbot for admission of National Economics University. Computers and Education: Artificial Intelligence, 2, Article 100036. https://doi.org/10.1016/j.caeai.2021.100036

Odede, J., & Frommholz, I. (2024). JayBot: Aiding university students and admission with an LLM-based chatbot. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval (CHIIR 2024) (pp. 391-395). Association for Computing Machinery. https://doi.org/10.1145/3627508.3638293

Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, Article 100033. https://doi.org/10.1016/j.caeai.2021.100033

Radhakrishnan, H. K., & Dias, N. G. J. (2023). Use of a chatbot-based advising system for the higher-education system. In Proceedings of the International Conference on Advances in Computing and Technology (ICACT).

Tamascelli, M., Bunch, O., Fowler, B., Taeb, M., & Cohen, A. (2025). Academic advising chatbot powered with AI agent. In Proceedings of the 2025 ACM Southeast Conference (pp. 195-202). Association for Computing Machinery. https://doi.org/10.1145/3696673.3723065

Vazquez-Cano, E., Mengual-Andres, S., & Lopez-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(1), Article 33, 1-20. https://doi.org/10.1186/s41239-021-00269-8

Yigci, D., Eryilmaz, M., Yetisen, A. K., Tasoglu, S., & Ozcan, A. (2025). Large language model-based chatbots in higher education. Advanced Intelligent Systems, 7(3), Article 2400429. https://doi.org/10.1002/aisy.202400429

DOI

10.62226/ijarst20262787

PAGES : 2526-2533 | 4 VIEWS | 2 DOWNLOADS

How do you cite this paper?

OHIA Paul Ndudiri and OLOJIDO Joseph Bamikole — “Design and Development of an AI-Powered Intelligent Chatbot for Student Information Services in Higher Educational Institutions Using Large Language Models.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 8. DOI: 10.62226/ijarst20262787.

Read / Download Full Article