AI-Powered Chatbots in Higher Education: A UTAUT2 and ECM Analysis

Abstract

Adopting new policies in higher education is always accompanied with technological change and it is the intention of the stakeholders to accept and implement this change. This research seeks to explore the determinants of technology acceptance with the specific interest in the case of mobile learning in the context of combining chatbots in higher education. The inquiry aims to investigate the adoption of chatbots in education as a means of teaching, considering the factors that hinder or promote the use of this technology in the educational processes. The research design employed was a mixed-methods approach and qualitative interviews and quantitative surveys were used to gather information on chatbot adoption. The university community comprising faculty, administrative staff, and students from various higher education institutions was the study population. Data collection utilized self-administered structured questionnaires and person to person semi structured interviews. Statistical data collection and analysis was computerized with the help of SPSS. Pearson correlation and regression analysis were used to assess the association between the constructs of UTAUT2 and practical usage of chatbots. Drawing on the UTAUT model and the ECM Expectation-Confirmation Model (ECM) and its augmentation by technology, this paper investigated the diffusion of AI based chatbots in higher education. The study found spirited drivers of chatbots adoption that included performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value and habit. The importance of UTAUT2 in the analysis of interruption of use of technology in the case of chatbots is very promising to the educational managers and policy makers as it provides opportunities to figure out causes of failure. The findings of the study present a certain applicability for educational organizations wishing to adopt a chatbot. To facilitate the acceptance and use of chatbots by faculty, staff, and students, there is a need to understand the reasons that contribute to the adoption of this technology. Educational leaders and policy makers can make use of these findings to ensure effective application of chatbots in teaching and learning hence enhancing the experience of users as well as improving the impact of education. This research building on existing research will further enhance appreciation of acceptance of technological innovations in learning especially adoption of chatbots. Utilizing there last two models of Isaias and Q–complete study provide additional factors in application of AI-based chatbots in higher education. What’s new to the study, however, is the attempt to predict factors that lead to the increased use of chatbots among learners, emphasizing the possible advantages and risks of implementing this technology in education settings.