Bridging the Attitude-Engagement Gap: Instructor Perceptions, Predictors, and Institutional Adoption of Artificial Intelligence in Higher Education

Abstract

Artificial intelligence (AI) is reshaping the landscape of higher education, yet empirical understanding of how university instructors perceive, engage with, and evaluate AI in their teaching contexts remains limited. This study examines university instructors' general perceptions, institutional AI engagement, student experience impact, concerns about AI, and future expectations regarding artificial intelligence in higher education, and investigates whether these dimensions differ by demographic and professional characteristics and which factors predict institutional AI engagement. A quantitative cross-sectional survey design was employed. Data were collected from N = 229 university instructors across five academic schools using the QUIA instrument with five subscales of five items each — General Perceptions (GPI), Current AI Use (UAI), Student Experience Impact (ISE), Concerns About AI (CAU), and Future Expectations (EAI) — scored on a five-point Likert scale. Instructors expressed positive agreement across all five dimensions. Future Expectations (EAI) were highest and Current AI Use (UAI) lowest, revealing an attitude-engagement gap. Concern about AI replacing teachers was the only item below the neutral midpoint. Female instructors reported significantly higher Concerns About AI (CAU) than male colleagues. School of Education instructors reported the highest institutional AI engagement. Student Experience Impact (ISE) and General Perceptions (GPI) were the primary psychological predictors of institutional AI engagement, while gender and School of Education affiliation were the significant demographic predictors. Institutions seeking to increase responsible AI adoption should prioritize structured, discipline-specific professional development that enables instructors to experience AI's pedagogical benefits directly.