Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

The Development of a Web Digital Assistant: A Chatbot for an Academic Unit of a University

Adedapo Adegoke Adejuwon, Christopher Osita Anyaeche

Abstract

Manual communication between students and academic departments is often time-consuming and inefficient, requiring significant staff effort to address routine inquiries. Existing chatbot solutions are further limited by rigid scripting and a lack of adaptability. This study aimed to develop a web- based artificial intelligence chatbot to enhance information dissemination between departmental administration and students. A descriptive analysis was conducted using data from a 14-item questionnaire administered to students in the selected department of the case study. The findings informed the design and development of the chatbot system. The application was implemented using web technologies, including HTML, JavaScript, Artificial Intelligence Markup Language , and Visual Studio Code, and evaluated through manual conversational testing. Results indicated that 78.2% of respondents were satisfied with the existing system, while 98.6% supported the introduction of a chatbot. Testing transcripts demonstrated that the chatbot accurately interpreted user queries and generated appropriate responses, attributable to effective training and pattern recognition mechanisms. The developed web-based chatbot provides instant access to frequently requested information, reducing administrative workload and improving student engagement. It also serves as a learning support tool by enabling rapid access to academic resources such as course materials, lecture schedules, and staff information.

Keywords

ChatbotArtificial IntelligenceNatural Language ProcessingAcademic Support SystemInformation Dissemination

References

Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006. https://doi.org/10.1016/j.mlwa.2020.100006 Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modelling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473 Følstad, A., & Brandtzaeg, P. B. (2020). Users’ experiences with chatbots: Findings from a questionnaire study. Quality and User Experience, 5(1), 1–14. https://doi.org/10.1007/s41233-020-00033-2 Pérez-Soler, S., Guerra, E., & de Lara, J. (2021). Automatic generation of conversational bots using model-driven engineering. Information and Software Technology, 134, 106557. https://doi.org/10.1016/j.infsof.2021.106557 Zhang, Z., Oh, Y. J., Lange, P., Yu, Z., & Fukuoka, Y. (2020). Artificial intelligence chatbot behaviour change model for health intervention. Journal of Medical Internet Research, 22(10), e22845. https://doi.org/10.2196/22845 Chaudhary, S., & Sharma, A. (2021). Conversational AI chatbots in education: A review and research agenda. International Journal of Educational Technology in Higher Education, 18(1), 34. https://doi.org/10.1186/s41239-021-00277-y Ventola, E. (2020). Learning in the age of conversational agents: A systematic review on AI chatbots for educational purposes. Computers & Education: Artificial Intelligence, 1, 100003. https://doi.org/10.1016/j.caeai.2020.100003 Huang, G., Zhou, Y., & Wu, S. (2022). Chatbot acceptance in higher education: An extended TAM perspective. Education and Information Technologies, 27(6), 7453–7474. https://doi.org/10.1007/s10639-022-11023-7 Alhindi, T., & Alkhalifa, M. (2023). Evaluating chatbot effectiveness and user satisfaction in university student services. Journal of Educational Computing Research, 61(5), 1175– 1198. https://doi.org/10.1177/07356331231155167 Zhou, L., & Ding, L. (2021). A deep learning approach to improve chatbot response accuracy in academic domains. Neural Computing and Applications, 33, 12297–12310. https://doi.org/10.1007/s00521-021-06146-4 Nguyen, T., & Nguyen, Q. (2023). Natural language processing techniques for robust chatbot designs: A comparative study. Artificial Intelligence Review, 56(2), 825–858. https://doi.org/10.1007/s10462-022-10185-z Al-Shammari, R. M., & Aladdad, B. (2022). Web-based information systems in higher education: Student satisfaction and performance outcomes. Education and Information Technologies, 27(2), 1841–1862. https://doi.org/10.1007/s10639-021-10789-y Dawson, P., & Kearney, S. (2021). Evaluating academic support chatbots: A framework for learning effectiveness. Educational Technology Research and Development, 69(3), 1549– 1571. https://doi.org/10.1007/s11423-021-10013-3 Kowatsch, T., Maass, W., & Ciallella, N. (2020). Measuring intelligent system performance: A survey of techniques for assessing chatbot accuracy and responsiveness. Journal of Systems and Software, 170, 110726. https://doi.org/10.1016/j.jss.2020.110726 Lee, K., & Lee, J. (2022). Student perceptions of AI chatbots for information retrieval in higher education. Computers & Education, 181, 104436. https://doi.org/10.1016/j.compedu.2022.104436 Rashid, T., & Asghar, H. M. (2021). Technology acceptance of AI chatbots: An empirical study in university student contexts. Journal of Educational Technology & Society, 24(1), 45– 60. Serdyukov, P., & Vasilyev, A. (2023). Ensuring fairness and transparency in NLP chatbots: A survey of recent approaches. ACM Computing Surveys, 56(9), 1–33. https://doi.org/10.1145/3486608 Sharma, P., Sabnis, S., Mane, Y., & Chauhan, A. (2019). Multimedia chatbot using classification. International Conference Proceedings, 1264–1268. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20, 56. Ayanwale, M. A., & Molefi, R. R. (2024). Exploring intention of undergraduate students to embrace chatbots: From the vantage point of Lesotho. International Journal of Educational Technology in Higher Education, 21, 20.

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY

Advances in Algorithmic Contract Scoring for Pre-Negotiation Yield Optimization and Risk Retention

Author: Ngozi Samuel Uzougbo, Michael Ominyi, Cyril Chimelie Anichukwueze, Blessing, Chika Jones

DevTest flow: Designing a Scalable Continuous Testing Pipeline for High-Velocity Software Delivery

Author: Lawal Ahmed Oladimeji, Achori Busayo, Akeju BusayoZainab, Saka Samson, Damilare, Mbah Demian Chidi, Runsewe Similoluwa Mayowa, Oladiti Luqman, Abiodun