Computer-based testing in higher education: a phenomenology investigation into undergraduate students’ perspectives through the technology acceptance model - Zakariya, Yusuf Feyisara; Alotaibi, Sarah Bader; Alrashood, Jawaher Saud; Alrosaa, Tahani Mohammed (Frontiers in Psychology, 2026)
BackgroundComputer-based testing (CBT) is widely adopted in Nigerian higher institutions, with even some national pre-university examinations now conducted through CBT. However, an in-depth investigation into the perceptions of users, especially students, is lacking.ObjectiveThis study, employing a phenomenological approach within the qualitative research paradigm, examines university students’ perceptions of CBT through the lens of the Technology Acceptance Model.MethodsUsing a self-developed interview protocol, we generated data through semi-structured individual interviews of undergraduate students purposively selected for this study. We analyzed the generated data using thematic analysis aided by ChatGPT-4o.ResultsThe results of this study show that university students viewed CBT as useful for fast grading and reduced administrative errors, though technical failures and rigid multiple-choice formats limited its perceived value. Ease of use was generally positive, especially among digitally skilled students, but server crashes and poor facilities undermined usability. Attitudes toward CBT were mixed, shaped by efficiency, fairness concerns, and exam-related stress. Behavioral intention to continue using CBT remained conditional: students were willing to adopt it if reliability, system support, and assessment flexibility improved. These results are consistent with TAM’s emphasis on usefulness, ease of use, and favorable attitudes in shaping technology acceptance.ConclusionThe findings have two key implications. First, universities should implement structured digital literacy training to ensure all students can navigate CBT effectively, reducing anxiety and improving confidence. Second, assessment policies should be refined to enhance fairness by incorporating diverse question formats and addressing inconsistencies in difficulty levels. We argue that addressing these concerns will improve student trust and engagement with CBT. Finally, we proposed a framework for AI-assisted qualitative data analysis as a contribution to the literature, among others.