A Chatbot for Postgraduate Information Dissemination.
| dc.contributor.author | Umar, E., Odulaja, G. O. & Babatunde, A. N. | |
| dc.date.accessioned | 2025-10-18T18:42:42Z | |
| dc.date.available | 2025-10-18T18:42:42Z | |
| dc.date.issued | 2025-04-17 | |
| dc.description.abstract | Information and Communication Technology (ICT) is defined as the acquisition, processing, storage and dissemination of information through the use of computer and related technologies. While information dissemination in most Nigerian tertiary institutions utilizes the potentials of the modern computer, the revolutionizing automation potentials of Artificial Intelligence Agents (AIA) has not been duly explored. This has resulted in communication gaps and delayed responses to crucial and time critical queries and requests. To curtail this lapses and to ensure timely delivery of information to users, this paper adopted mixed method research (i.e. design based approach and action research). Incremental software development model was used for software development, Decision Tree Algorithm was used for training the system whose database was populated with relevant and likely questions and corresponding responses. The Chatbot was designed using HTML, CSS, and ReactJava programming languages while the database was designed using PHP and MySQL Database. The result was a selfreporting, self-learning and interactive Chatbot for postgraduate information dissemination that autonomously create corresponding responses to user’s queries in real time. User’s Intent Understanding (UIU), Query Response Accuracy (QRA), Error Handling (EH) and Mean Response Time (MRT) were some of the metrics used for user -system’s performance evaluation. Using a self-structured questionnaire, responses obtained from 130 student users purposively selected in TASUED with similar academic characteristics and needs were analysed using IBM SPSS v20. Findings show that on average, the system achieved 89% on UIU, 89% on QRA, less than 2 seconds on MRT (depending on processor’s speed) and 95% on EH for all cases considered. The paper thus concludes that the decision tree algorithm is effective and efficient for developing self-reporting and self-learning chatbots and recommends further improvements based on these metrics using other machine learning algorithms. Keywords: TASUED, COSIT, Self-reporting, Chatbot, Information Dissemination. | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/5936 | |
| dc.publisher | Faculties of Science and Engineering of the Nigeria Defense Academy, Jaji, Kaduna State | |
| dc.title | A Chatbot for Postgraduate Information Dissemination. |
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