The rapid diffusion of generative artificial intelligence across higher education has transformed the ways in which students, instructors, and institutions conceptualize learning, assessment, and academic work. While large language models and related generative tools promise unprecedented efficiency, creativity, and scalability, their integration into educational practice has also provoked significant concerns regarding academic integrity, learning quality, cognitive dependency, and the erosion of critical thinking. The present study advances a comprehensive theoretical and interpretive investigation of generative artificial intelligence adoption in higher education by integrating task–technology fit, technology acceptance, and experience–technology alignment perspectives into a unified analytical framework. Grounded in contemporary empirical and conceptual scholarship, this research examines how generative AI tools align with the epistemic, procedural, and evaluative tasks that characterize modern higher education, with special emphasis on assessment, test automation, and behavior-driven development in digital learning environments. Particular attention is given to the implications of generative AI for automated testing and assessment design, drawing on the work of Tiwari (2025), who demonstrated how generative AI-driven behavior-driven development can enhance the efficiency, consistency, and scalability of test automation processes. This contribution is extended into the higher education domain, where assessment systems increasingly rely on automated and semi-automated tools that mirror industrial software testing architectures.

Using a theoretically grounded qualitative synthesis approach, this study integrates evidence from international surveys, institutional reports, and conceptual models to interpret how students and faculty perceive, adopt, and utilize generative AI across disciplines. The findings reveal that generative AI adoption is not merely driven by perceived usefulness or ease of use but is fundamentally shaped by the degree to which these technologies fit the cognitive, disciplinary, and evaluative tasks faced by learners and educators. When task–technology fit is high, generative AI becomes an enabling infrastructure for deeper engagement, adaptive feedback, and more authentic assessment design. When misalignment occurs, however, the same technologies can undermine learning, amplify academic misconduct, and distort performance evaluation.

The study contributes to theory by demonstrating that traditional acceptance models such as TAM and UTAUT must be reinterpreted in light of generative AI’s co-creative and autonomous capacities, which fundamentally alter the nature of academic tasks. It also contributes to practice by offering a nuanced understanding of how generative AI can be ethically and pedagogically embedded into higher education assessment systems. By bridging software engineering perspectives on automated testing with educational theories of learning and evaluation, this research provides a robust conceptual foundation for navigating the generative AI transition in higher education.

Generative artificial intelligence, task–technology fit, higher education

Kelly, A.; Sullivan, M.; Strampel, K. Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching and Learning Practice, 2023, 20, 12.

Tiwari, S. K. Automating Behavior-Driven Development with Generative AI: Enhancing Efficiency in Test Automation. Frontiers in Emerging Computer Science and Information Technology, 2025, 2(12), 01–14.

Jung, T.; Bae, S.; Moorhouse, N.; Kwon, O. The effects of experience–technology fit on consumption behavior: Extended reality visitor experience. Information Technology and People, 2023, 37, 2006–2034.

Abbas, M.; Jam, F. A.; Khan, T. I. Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 2024, 21, 10.

Furneaux, B. Task–technology fit theory: A survey and synopsis of the literature. Information Systems Theory, 2012, 1, 87–102.

Pan, Z.; Xie, Z.; Liu, T.; Xia, T. Exploring the key factors influencing college students’ willingness to use AI coding assistant tools: An expanded technology acceptance model. Systems, 2024, 12, 176.

Wu, B.; Chen, X. Continuance intention to use MOOCs: Integrating the technology acceptance model and task–technology fit model. Computers in Human Behavior, 2017, 67, 221–232.

Johnston, H.; Wells, R. F.; Shanks, E. M.; Boey, T.; Parsons, B. Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal of Educational Integrity, 2024, 20, 2.

Przegalinska, A.; Triantoro, T.; Kovbasiuk, A.; Ciechanowski, L.; Freeman, R.; Sowa, K. Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task–technology fit perspectives. International Journal of Information Management, 2024, 81, 102853.

Ngo, T. T. A. The perception by university students of the use of ChatGPT in education. International Journal of Emerging Technologies in Learning, 2023, 18, 4–19.

Strzelecki, A.; ElArabawy, S. Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students. British Journal of Educational Technology, 2024, 55, 1209–1230.

Zhou, X.; Zhang, J.; Chan, C. Unveiling students’ experiences and perceptions of artificial intelligence usage in higher education. Journal of University Teaching and Learning Practice, 2024, 21, 1–20.

Malmstrom, H.; Stohr, C.; Ou, W. Chatbots and other AI for learning: A survey of use and views among university students in Sweden. Chalmers Studies in Communication and Learning in Higher Education, 2023.

Sun, J.; Guo, Y. A new destination on the palm? The moderating effect of travel anxiety on digital tourism behavior in extended UTAUT2 and task–technology fit models. Frontiers in Psychology, 2022, 13, 965655.

Hosseini, M.; Gao, C. A.; Liebovitz, D. M.; Carvalho, A. M.; Ahmad, F. S.; Luo, Y.; MacDonald, N.; Holmes, K. L.; Kho, A. An exploratory survey about using ChatGPT in education, healthcare, and research. PLoS ONE, 2023, 18, e0292216.

Krause, S.; Panchal, B.; Ubhe, N. The evolution of learning: Assessing the transformative impact of generative AI on higher education. In International Conference on Artificial Intelligence in Education Technology, Springer Nature, Singapore, 2024, 356–371.

Zhou, T.; Lu, Y.; Wang, B. Integrating task–technology fit and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 2010, 26, 760–767.

Al-Maatouk, Q.; Othman, M. S.; Aldraiweesh, A.; Alturki, U.; Al-Rahmi, W. M.; Aljeraiwi, A. A. Task–technology fit and technology acceptance model application to structure and evaluate the adoption of social media in academia. IEEE Access, 2020, 8, 78427–78440.

Elci, A.; Abubakar, A. M. The configurational effects of task–technology fit, technology-induced engagement and motivation on learning performance during COVID-19 pandemic. Education and Information Technologies, 2021, 26, 7259–7277.

Lafi, G. A. The effect of knowledge management systems on organizational ambidexterity: A conceptual model. Journal of Economics, Management and Trade, 2023, 29, 40–51.

Von Garrel, J.; Mayer, J. Artificial intelligence in studies: Use of ChatGPT and AI-based tools among students in Germany. Humanities and Social Sciences Communications, 2023, 10, 799.

Grájeda, A.; Burgos, J.; Cordova, P.; Sanjines, A. Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 2024, 11, 2287917.

UNESCO. Harnessing the Era of Artificial Intelligence in Higher Education: A Primer for Higher Education Stakeholders. UNESCO IESALC, Caracas, 2023.

IDC. Unlock the potential of GenAI use cases across your enterprise. International Data Corporation, 2023.

Gartner. Nine economic implications of generative AI for strategic decision making. Gartner Research, 2023.

Kurtz, G.; Amzalag, M.; Shaked, N.; Zaguri, Y.; Kohen-Vacs, D.; Gal, E.; Zailer, G.; Barak-Medina, E. Strategies for integrating generative AI into higher education. Education Sciences, 2024, 14, 503.

Carver, R.; Nash, J. Doing Data Analysis with SPSS: Version 18.0. Cengage Learning, Belmont, 2011.

Pallant, J. SPSS Survival Manual. McGraw-Hill Education, New York City, 2013.

Al-kfairy, M. Factors impacting the adoption and acceptance of ChatGPT in educational settings. Applied System Innovation, 2024, 7, 110.