From Novelty to Normalisation: Tracking Changing Perceptions of AI in Higher Education, 2024-2026

• URL: http://arxiv.org/abs/2607.16223v2
• Date: Tue, 21 Jul 2026 14:52:26 GMT
• Title: From Novelty to Normalisation: Tracking Changing Perceptions of AI in Higher Education, 2024-2026
• Abstract summary: The rapid integration of generative artificial intelligence has reshaped the landscape of higher education.<n>Students have embraced tools such as ChatGPT with striking speed, while teaching staff and institutions have responded with greater caution.<n>This paper presents a longitudinal study of AI perceptions in higher education, tracking undergraduates, doctoral researchers, teaching staff and non-teaching staff at Ulster University.
• Score: 1.701796716399534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid integration of generative artificial intelligence (AI) has reshaped the landscape of higher education. Students have embraced tools such as ChatGPT with striking speed, while teaching staff and institutions have responded with greater caution. Existing research on AI perceptions has mainly been cross-sectional, providing single-point snapshots that view attitudes as stable rather than evolving. This paper presents a longitudinal study of AI perceptions in higher education, tracking undergraduates, doctoral researchers, teaching staff and non-teaching staff at Ulster University across three survey waves between 2024 and 2026 (n=1,665). A quantitative survey design measured familiarity, reported use and perceived risk; results show that students rapidly normalised AI use over the period, moving from tentative experimentation to routine engagement, while staff expressed persistent concerns about academic integrity, assessment design, and critical thinking. Doctoral and non-teaching staff occupied intermediate positions, reflecting both pragmatic adoption and institutional caution. The student-staff gap widened as institutional policy struggled to keep pace with actual practice. By tracking these shifts directly rather than reconstructing them from disconnected studies, the paper moves beyond descriptive accounts of AI attitudes and demonstrates the importance of capturing perceptions in real time. The findings carry significant implications for adaptive institutional policy, AI literacy initiatives, and targeted staff training.

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