Trusting the Machine: University Faculty Perspectives on AI in Information Technology Education

Abstract

This study examines university faculty acceptance of artificial intelligence (AI) tools in information technology education, addressing gaps in trust, ethical alignment, and institutional readiness. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), the study deployed a structured survey among 348 faculty respondents from the Philippines, China, Malaysia, Qatar, and Vietnam. Exploratory Factor Analysis confirmed four latent constructs—Trust, Usefulness, Ease of Use, and Behavioural Intention—with strong factor loadings (> 0.60), high sampling adequacy (KMO = 0.915), and significant sphericity (Bartlett's Test: χ² = 6342.51, p < .001). Multiple regression analysis yielded a considerable model (F(3, 344) = 57.81, p < .001), explaining 52.3% of the variance in Behavioural Intention (Adjusted R² = 0.523). Perceived usefulness was the strongest predictor (β = 0.551, p < .001), followed by trust in AI (β = 0.224, p < .001) and ease of use (β = 0.185, p = .001). Results highlight the need for institutional transparency, faculty training, and ethical guidance to foster responsible AI adoption in higher education.

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