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Generative AI in Action: Acceptance and Use among Higher Education Staff Pre- and Post-Training

2025·1 ZitationenOpen Access
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1

Zitationen

3

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2025

Jahr

Abstract

The adoption of generative artificial intelligence (genAI) in higher education (HE) has led to an eminent need to foster genAI literacy among staff. In this longitudinal study, we examined if participation in a genAI introductory course was associated with an increased intention to use genAI tools among HE staff through a one-group pre/posttest design. We posed five hypotheses based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and found a significant and large increase in use from the beginning (T1) to the end (T2) of the course. Similarly, all UTAUT2 predictor variables significantly increased while AI attitudes did not. Through PLS-SEM we discovered that Habit and AI attitudes were significant predictors of Behavioral intentions at T1, but that none of the examined variables significantly predicted Behavioral intentions at T2. Behavioral intentions at T1 did not significantly predict Behavioral intentions at T2, either. However, the UTAUT2 factors combined with AI attitudes explained 79.5% of the variance in Behavioral intentions at T1 and 82.4% at T2. Neither gender, age, experience with genAI, nor teaching position moderated the relationship between AI acceptance at T1 and T2. Our study is the first to implement a pre/post design utilizing the UTAUT2 framework among HE staff. Our findings suggest that course participation is associated with changes in prior intention patterns, potentially reducing disparities in familiarity and use, thereby facilitating professional development within a collegial community. Our novel results have implications for future research and HE institutional professional development worldwide.

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Artificial Intelligence in Healthcare and EducationBig Data and Business IntelligenceEngineering Education and Technology
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