A Framework for institutional change in the age of AI

TL;DR

This paper proposes a new framework for institutional change in STEM higher education to adapt to the rapid and uncertain impact of generative AI, emphasizing local inquiry, pedagogical focus, and collective engagement.

Contribution

It introduces a six-dimension framework for rethinking change models in the AI era, addressing tools, change agents, and students under conditions of uncertainty.

Findings

Framework identifies six key dimensions for AI-era educational change.

Design implications include focusing on pedagogical approaches over specific tools.

Case study demonstrates application of the framework in a university physics department.

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.