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A Campus-Wide AI-Driven Information Retrieval and Support System for Igbinedion University Okada

Samuel Adebowale Adekoya, Kingsley Ohiagu, Osaremwinda Omorogiuwa, Deinyefa, Godfree Igbiriki

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed information retrieval, making it more efficient, interactive, and context-aware. This project addressed the limitations of the existing information retrieval system at Igbinedion University, Okada, which did not keep pace with the capabilities offered by modern AI technologies. To overcome these shortcomings, a campus-wide AI-Driven Information Retrieval and Support System was developed. This solution leverages Retrieval-Augmented Generation and AI-powered Natural Language Processing to provide accurate, context-sensitive responses to queries. The system integrates a vector database and an intuitive web interface, implemented using JavaScript, to process and retrieve data efficiently from diverse university sources. The Structured Systems Analysis and Design Methodology was adopted to guide the project through all phases from feasibility study to implementation and testing. This AI-driven solution was designed to enhance access to university resources and provide a scalable foundation for future features, ultimately bridging the gap between users and institutional information.

Keywords

Artificial Intelligence (AI)Retrieval-Augmented Generation ; Vector Database; Information Retrieval (IR); Chatbot; Natural Language Processing ; Role-Based Access Control ; AI Models; AI SDKs

References

Baeza-Yates, R. & B. Ribeiro-Neto. (2011). Modern Information Retrieval: The Concepts and Technology Behind Search. Pearson, London. Cambria, E., S. Poria, A. Gelbukh, & M. Thelwall. (2017). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6), 74–80. Chen, T., Chen, J. & Y. Lin. (2020). Unleashing AI in education. J. Inf. Sci. Eng., 36(2), 123-145. Chen, J. et al. (2022). ADVANCING REAL-TIME CONTEXT-AWARE RAG SYSTEMS WITH MULTI-MODAL DATA INTEGRATION. Copyright.com. (2023). Keyword-Search-Tip-Sheet. Deakin University. (2020). Genie Digital Assistant. Devlin, J., M. Chang, K. Lee, & K. Toutanova. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805. Izacard, G. & E. Grave. (2021). Retrieval-Augmented Generation: A Comprehensive Survey of. arXiv preprint arXiv:2403.14197v1. Johnson, R., A. Patel, M. & L. Williams. (2024). Artificial Intelligence in Information Retrieval. Bpasjournals.com, 4(2), 52–65. Karpukhin, V., B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, & W. Yih. (2020). Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 6769-6781. Kasela, P., et al. (2025). PARK: Personalized academic retrieval with knowledge-graphs. arXiv:2507.13910. Kim, S. (2021). AI systems in silos. Lee, M. & V. Kumar. (2023). Query Refinement into Information Retrieval Systems. Journal of Information and Organizational Sciences, 47(1), 133–151. Lewis, P., E. Perez, A. Piktus, et al. (2020). Retrieval-Augmented Generation for Knowledge- Intensive NLP Tasks. In Advances in Neural Information Processing Systems, 33. Luong, H. & Luong, K. (2025). A Chatbot-Based Academic Advising Model for Student in Information Technology: A Case Study. Saudi J Eng Technol, 10(3), 93-100. Manning, C., P. Raghavan, & H. Schütze. (2008). Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK. Page, L. & Gehlbach, H. (2017). Summer Melt. Harvard Education Press. Shuster, K., D. Ju, S. Roller, et al. (2021). Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567. Staffordshire University. (2021). Beacon app. Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, & I. Polosukhin. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems, 30. Zawacki-Richter, O., M. D. Hillmayr, & J. Stöhr. (2019). The Role of AI, Big Data and Learning Analytics in Higher Education. Zeitschrift für Bildungsforschung, 6(1), 1–25.

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