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

2026-06-18

AI Summary

This study addresses a critical gap in existing research, which predominantly relies on cross-sectional data and lacks longitudinal insights into the evolving perceptions of artificial intelligence (AI) in higher education. Conducted at Ulster University from 2024 to 2026, the project employed three waves of longitudinal surveys tracking 1,665 undergraduate students, doctoral candidates, and academic staff to examine shifts in AI familiarity, usage behaviors, and risk perceptions. For the first time through a longitudinal lens, it reveals how AI awareness transitions from novelty to normalization: students rapidly integrate AI into their learning practices, whereas faculty members consistently express concerns about academic integrity and assessment challenges, thereby exposing a widening cognitive divide. The findings underscore that institutional policies lag behind actual AI adoption, highlighting an urgent need for dynamic governance frameworks and targeted AI literacy initiatives.

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.

Problem

• generative artificial intelligence
• higher education
• longitudinal study
• perceptions
• AI normalisation

Innovation

• longitudinal study
• generative AI
• perception tracking
• higher education
• AI normalization