Autonomy Without Alignment: Professional Development and the Fragmented AI Landscape in Higher Education

Authors

Kelly D. Muller, Winona State UniversityFollow

Abstract

Higher education’s strong commitment to autonomy and academic freedom has produced a fragmented and ethically inconsistent approach to artificial intelligence (AI) professional development. As AI reshapes teaching, assessment, authorship, and academic integrity, faculty responses range from prohibition to enthusiastic adoption, often without shared institutional guidance. Current professional development efforts are largely optional, tool-focused, and reactive, leaving instructors to construct individual ethical frameworks while students encounter inconsistent expectations across courses. This variability raises concerns about equity, accountability, and institutional risk. Drawing on scholarship related to academic freedom, AI governance, and diffusion of innovation, the article proposes a three-layer model that reframes AI professional development as shared instructional infrastructure: required institutional foundations, discipline-specific contextualization, and protected individual pedagogical freedom. By distinguishing collective responsibilities from individual discretion, this framework preserves academic freedom while promoting ethical alignment, coherence, and equitable student preparation in an AI-mediated higher education landscape.

Recommended Citation

Muller, Kelly D. (2026) "Autonomy Without Alignment: Professional Development and the Fragmented AI Landscape in Higher Education," Essays in Education: Vol. 32: Iss. 1, Article 8.
Available at: https://openriver.winona.edu/eie/vol32/iss1/8

Primary Author Bio Sketch

Kelly Muller

Education Doctorate student at Winona State University and Clinical Assistant Professor in the Speech, Language, and Hearing Sciences department at the University of Wisconsin Eau Claire (UWEC)