Faculty Orientations Shape Adoption of AI in Research and Teaching

TL;DR

This study explores how faculty's pedagogical orientations influence their adoption of AI tools in research and teaching, revealing that disciplinary views, rather than institutional factors, are key drivers.

Contribution

It introduces the concept of AI pedagogical orientation as a predictor of AI adoption among faculty, highlighting the importance of disciplinary perspectives over institutional initiatives.

Findings

AI pedagogical orientation predicts AI use across activities.

Disciplinary views influence AI adoption more than institutional factors.

Existing models may not fully explain technology adoption in disciplinary contexts.

Abstract

Despite the widespread availability of large language models (LLMs) in higher education, instructors vary substantially in their adoption and use of these tools, and the reasons for this variation remain poorly understood. A mixed-methods survey of 90 STEM faculty in the Research Corporation for Science Advancement (RCSA) Cottrell community examined relationships between AI use, attitudes, institutional context, and instructional practice. Exploratory factor analysis identified a coherent construct, \textit{AI pedagogical orientation}, that strongly predicted self-reported AI use across research, teaching, and other professional activities. Qualitative analysis indicated that this construct reflected differing views about the role AI should play in disciplinary thinking, learning, and expertise development, rather than simply positive or negative attitudes toward AI. Institutional initiatives, demographic variables, and information sources showed comparatively weak associations with AI use. The results suggest that existing technology-adoption models may not fully explain adoption in contexts where technologies interact directly with disciplinary reasoning and knowledge production.