🤖 AI Summary
Generative AI, as an “arrival technology,” has entered classrooms before robust pedagogical evidence has matured, thereby challenging conventional models of STEM higher education reform that rely on stable empirical foundations. This study proposes a new institutional change framework tailored for the AI era, reconfiguring the logic of educational transformation across six dimensions—three technological (evidence base, pace of change, scope of application) and three human (faculty, change agents, students). The framework emphasizes humble, context-sensitive exploration; pedagogy-centered design; reconceptualizing change agents as facilitators of collective inquiry; and positioning students as partners in reform. Drawing on theories of educational change, analyses of generative AI applications, and illustrative case studies, the work constructs and validates an adaptive, collaborative, and dynamically evolving model that offers the first systematic response to the demands of transforming higher education within highly uncertain technological environments.
📝 Abstract
Generative AI is rapidly reshaping STEM higher education. Not only are our educational practices changing, but how we think about educational transformation must adapt. Existing models of institutional change in STEM, aimed at interactive engagement, have largely followed an adoption logic: relatively stable, well-researched educational practices are evaluated and then scaled. These assumptions do not hold for generative AI, which is an arrival technology -- entering classrooms before a sufficient pedagogical evidence base could form. Building on recent decades of work on STEM institutional change, we propose a framework identifying six dimensions along which prior change models must be reconsidered in light of AI: three concerning the tools at the center of reform (the tool's evidence base, rate of change, and scope), and three concerning the people involved in change (faculty, change agents, and students). For each dimension, we examine how AI-era assumptions differ from those underlying prior interactive engagement reforms and derive design implications, including: privileging humble and local inquiries; organizing reform around pedagogical approaches rather than specific tools; repositioning change agents as facilitators of collective inquiry; and engaging students as partners in reform. Collectively, the six dimensions and design implications constitute a new framework for adapting change models to support institutions under conditions of genuine uncertainty. Finally, we illustrate how the framework may be applied through a brief case-study of a faculty workshop series carried out in a university physics department to support instructors adapting to this modern AI era.