Research Article

Higher education students’ ChatGPT use behavior: Structural equation modelling of contributing factors through a modified UTAUT model

Valentine Joseph Owan 1 2 , Ibrahim Abba Mohammed 3 * , Ahmed Bello 3 , Tajudeen Ahmed Shittu 3
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1 Department of Educational Psychology, University of Calabar, Calabar, Cross River State, NIGERIA2 Ultimate Research Network, Calabar, Cross River State, NIGERIA3 Department of Science Education, Federal University of Kashere, Kashere, Gombe State, NIGERIA* Corresponding Author
Contemporary Educational Technology, 17(4), October 2025, ep592, https://doi.org/10.30935/cedtech/17243
Published: 08 October 2025
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ABSTRACT

Despite the increasing interest in artificial intelligence technologies in education, there is a gap in understanding the factors influencing the adoption of ChatGPT among Nigerian higher education students. Research has not comprehensively explored these factors in the Nigerian context, leaving a significant gap in understanding technology adoption in this setting. This study addressed this gap by investigating the predictors of students’ behavioral intentions (BIs) and actual use behavior of ChatGPT through the lens of the unified theory of acceptance and use of technology 2 (UTAUT2) framework. A cross-sectional correlational research design was used to examine the relationships between extended UTAUT variables, BIs, and ChatGPT use behavior. A sample of 8,496 higher education students from diverse institutions in Nigeria participated in the study. The data were collected using the higher education students’ ChatGPT utilization questionnaire, which assessed various factors, such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), hedonic motivation (HM), habit (HB), BI, and ChatGPT use behavior. The findings reveal several significant predictors of students’ BIs and actual usage of ChatGPT. PE, SI, HM, and HB were found to be significant positive predictors of BI, while EE and FCs were significant negative predictors. For ChatGPT use behavior, FCs, HM, HB, and BI were significant positive predictors, whereas PE and SI were significant negative predictors. BI mediated the relationships between several factors and ChatGPT usage behavior: positively for some (PE, SI, HM, and HB) and negatively for others (EE and FC). This study contributes to understanding the adoption of ChatGPT in higher education contexts. The findings highlight the importance of addressing usability issues, providing adequate support and resources, promoting a positive user experience, fostering habitual usage, and leveraging social networks to encourage adoption.
Keywords: artificial intelligence, chatbots, GenAI, large language models, technology use

CITATION (APA)

Owan, V. J., Mohammed, I. A., Bello, A., & Shittu, T. A. (2025). Higher education students’ ChatGPT use behavior: Structural equation modelling of contributing factors through a modified UTAUT model. Contemporary Educational Technology, 17(4), ep592. https://doi.org/10.30935/cedtech/17243

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