Generational Differences in the Academic Integration of Artificial Intelligence in Higher Education

Abstract

The accelerated integration of artificial intelligence (AI) in higher education is reshaping academic practices, particularly within engineering and technology-oriented environments. Despite this transformation, empirical evidence regarding generational differences in the academic adoption of AI remains limited. This study examined generational patterns in the academic integration of AI among 301 university students, assessing three dimensions: academic use of AI, trust in AI systems, and ethical awareness. Using a validated three-dimensional instrument (CFI = .974; RMSEA = .051), results indicated moderate levels of academic use (M = 2.92), slightly higher levels of trust (M = 3.05), and comparatively lower levels of ethical awareness (M = 2.73). Pearson correlations revealed significant positive associations among all dimensions (p < .001). No statistically significant differences were found by gender. However, Welch’s ANOVA showed a significant age effect on academic AI use (F = 7.02, p < .001), with participants aged 43 years or older reporting significantly lower utilization. No generational differences were observed in trust or ethical awareness. These findings suggest that generational disparities in AI integration are primarily behavioral rather than attitudinal, posing important implications for university AI governance, curriculum design, and the implementation of differentiated training strategies within digitally transforming educational ecosystems.