Adoption ChatGPT in higher education settings: Potentials, challenges, and paving the way for future advancements

Abstract

Several artificial intelligence generative language models have been developed recently. Many people in different contexts have started using them, such as education, industry, and even content creation. This study focuses on investigating how higher education students have been using ChatGPT as an example of a generative artificial intelligence tool. The context of the study was chosen due to the many voices against or in favor of using ChatGPT in higher education institutions. We deployed a mixed method to collect and analyze the data, including open-ended and closed-ended question surveys, to investigate the motivations and future expectations of students who have been using the ChatGPT tool. The research methodology comprises two main components: the initial phase involves conducting statistical analyses on questions about each of the 15 adoption factors. Responses concerning each factor were meticulously compiled and transferred into an Excel spreadsheet. Subsequently, the frequency of responses for each choice was tallied to discern prevailing trends and preferences among respondents. The second phase focuses on analyzing the open-ended questions' responses that forecast the future of ChatGPT as perceived by the participants. The data gathered from open-ended questions was analyzed thematically using Miles and Huberman's approach. Our findings reveal that students generally find using ChatGPT to be a helpful tool in their higher education institutions. The findings show that ease of use, perceived value, trialability, observability, relative advantage, social impact, and network effect are the most important factors behind their adoption decision. The findings also revealed that a significant number of students are still confused about ethical concerns and report the need for regulations when using ChatGPT. These findings are important for educators, policymakers, and technology designers who want to make the most of generative AI in education. Future research, however, is yet to be conducted to understand its ethical implications, long-term effects, and how to best incorporate it into teaching practices.

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