University students frequently encounter difficulties in accessing clear and timely information related to enrollment procedures and tuition payments, which can result in administrative delays and increased workload for academic offices. This paper presents the design and evaluation of an intelligent chatbot developed to assist students by automatically responding to inquiries related to enrollment and tuition support. The proposed system integrates machine learning (ML) and natural language processing (NLP) techniques to interpret user queries and deliver accurate, context-aware responses. The usability of the chatbot was quantitatively evaluated by measuring the time required for users to complete key tasks, including account registration, query execution, and system un-subscription, with an average completion time of approximately three minutes per task. Additionally, a user satisfaction study was conducted with sixty students to assess system performance in terms of ease of use, response speed, behavioral appropriateness, confidence in future use, and quality of responses. Results show that all evaluated criteria achieved average scores above 3 on a 5-point Likert scale. These findings indicate that the proposed chatbot is efficient, user-friendly, and well accepted by students, demonstrating its potential as a scalable digital solution for supporting university enrollment and tuition-related services.
Vanaja Kumari Degala (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: