Faculty Goal Orientation, Technology Perceptions, and the Adoption of GenAI in Higher Education

Abstract

Generative artificial intelligence (GenAI) use continues to rise in higher education spaces, yet faculty adoption remains inconsistent. Faculty GenAI adoption decisions may be influenced by motivation, technology perceptions, and professional context. This study examined how faculty goal orientations (GOT; mastery-approach [MAP], mastery-avoidance [MAV], performance-approach [PAP], performance-avoidance [PAV]) predicted GenAI adoption in teaching and research contexts, both directly and indirectly through technology beliefs identified by the Unified Theory of Acceptance and Use of Technology (UTAUT; performance expectancy [PE], effort expectancy [EE], social influence [SI], facilitating conditions [FC]).

A total of 293 faculty members employed at higher education institutions across the United States completed an online survey assessing goal orientation, UTAUT beliefs, and GenAI adoption. Separate observed-variable path analyses were conducted for teaching (N = 250) and research (N = 170) contexts using a bootstrapped mediation analysis examining indirect effects.

Findings indicated that GOT did not directly predict GenAI adoption in either teaching or research context; instead, it operated through UTAUT beliefs. In the teaching model, MAV orientation significantly predicted PE, EE and SI, with a significant indirect effect on teaching adoption through PE. In the research model, PAP orientation significantly predicted EE, SI and FC, with a significant positive indirect effect on research adoption through EE. PAV orientation negatively predicted PE, EE, and SI, with a significant negative indirect effect on research adoption through PE. Across both models, PE was consistently the strongest predictor of adoption, while SI and FC were not significant predictors in either context. Exploratory analyses revealed that teaching modality significantly predicted teaching GenAI adoption, while institutional support, race, and gender were associated with differences in research GenAI adoption.

Taken together, these findings suggest that faculty motivation does not directly influence GenAI adoption but instead works indirectly through the technology beliefs it produces. The processes connecting faculty motivation and technology perceptions operate differently between teaching and research contexts. This study offers practical guidance for higher education institutions building faculty development programs around GenAI: rather than a one-size-fits-all approach, programs may be more effective when targeting role-specific beliefs about GenAI’s usefulness, with differentiated strategies for teaching and research applications.