Evaluating the Effectiveness of ChatGPT in Web-Based Information Systems: An Empirical Study on User Interaction and System Performance

Abstract

The rapid advancement of generative artificial intelligence, particularly ChatGPT, has significantly transformed the landscape of web-based information systems by enabling more interactive, adaptive, and efficient user experiences. Despite its widespread adoption, empirical evidence evaluating its effectiveness in enhancing user interaction and system performance remains limited. This study aims to assess the effectiveness of ChatGPT integration within web-based information systems, focusing on its impact on user interaction quality and overall system performance. An empirical approach was employed using a quantitative research design, with data collected from users interacting with a ChatGPT-powered web system via structured questionnaires and system performance metrics. The analysis used statistical methods to examine the relationships between ChatGPT utilization, user engagement, response accuracy, and system efficiency. The findings indicate that ChatGPT significantly improves user interaction by providing faster responses, more natural communication, and higher engagement levels. Additionally, the integration of ChatGPT improves system performance, particularly response time and task completion efficiency, though minor limitations in contextual understanding and occasional inaccuracies persist. These results suggest that ChatGPT is a valuable component for enhancing the functionality of modern web-based systems. In conclusion, the study highlights the importance of integrating generative AI technologies to optimize digital system performance while emphasizing the need for continuous monitoring and refinement to address inherent limitations, thereby ensuring reliable and user-centered system development.