To Adopt or Else? Mapping Faculty AI Integration Profiles and Systemic Barriers

• In Event: AERA Roundtable Session Saturday 9:45 am - Gold 3
  In Roundtable Session: Technology, Pedagogy, and Student Learning

Abstract

As higher education grapples with generative AI, assuming uniform faculty resistance or readiness overlooks crucial heterogeneity. This study moves beyond individualistic models to map distinct faculty AI integration profiles and identify the systemic barriers that define them. Using model-based clustering on survey data from 202 faculty across three diverse institutions, we identified five distinct profiles—from “Enthusiastic Adopters” to “Overwhelmed Newcomers.” Crucially, profile membership correlated significantly with institutional context, tenure status, and race/ethnicity, revealing systemic inequities in AI engagement. These findings reject a one-size-fits-all approach, providing an empirical foundation for developing targeted, context-aware institutional strategies to support diverse faculty needs in an era of technological change.

Authors

• Chunling Niu, University of the Incarnate Word

• Soheila Sadeghi, University of the Incarnate Word

• Biao Ma, University of the Incarnate Word

• Theresa Garfield Dorel, Texas A&M University - San Antonio

• Melissa D. Portugal, University of the Incarnate Word

• Brian Waltman, University of the Incarnate Word

• Loren T. Cossette, University of the Incarnate Word