Higher-Education Faculty Adoption of Generative AI in Lesson Planning and Assessment

Abstract

This study investigates the primary predictors of higher-education faculty’s Behavioral Intention (BI) to adopt generative AI (GenAI) for lesson planning and assessment, utilizing an extended Technology Acceptance Model (TAM). A cross-sectional survey was administered to 60 faculty members, and the data was analyzed using multiple linear regression. The core findings revealed that while faculty hold high overall perceptions of GenAI’s usefulness and ease of use, Attitude toward Use (ATT) emerged as the dominant, significant predictor of $\mathrm{BI}(\beta=.703, \mathrm{p}\lt.001)$, fully mediating the effects of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Furthermore, the structural antecedents were confirmed: AI Self-Efficacy (SE) significantly predicts PEOU ($\boldsymbol{\beta}=. \mathbf{8 2 0}, \mathbf{p}\lt. \mathbf{0 0 1}$), and Compatibility (COMP) strongly predicts $\mathrm{PU}(\beta =.399, \mathrm{p}\lt.001)$. Critically, this COMP-PU relationship was found to be not moderated by the type of AI tool used (Specialized vs. General). External factors, including Subjective Norm and Perceived Risk/Concern, did not uniquely influence BI. These results emphasize that the psychological readiness and pedagogical fit of GenAI are more critical barriers to adoption than structural support or moderate concerns. Institutions are advised to focus on targeted training to boost self-efficacy and showcase GenAI’s seamless integration into existing teaching workflows to drive successful adoption.

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