A Framework for Institutional Change in the Age of AI

Created: 2026-05-14 | Tags: ai-educationinstitutional-changefaculty-developmenthigher-edpedagogystem-education
Perl-Nussbaum & Finkelstein (2026) adapt institutional-change models to generative AI as an arrival technology — one that entered classrooms before pedagogical evidence existed — yielding a six-dimension framework and design implications for leading change under uncertainty (faculty-development, higher-ed, policy-maker).

Core Contribution

Perl-Nussbaum & Finkelstein (2026) propose a six-dimension framework for adapting institutional change models in STEM higher education to the realities of generative AI. Unlike prior reforms based on adoption technologies (stable, evidence-based practices like Peer Instruction or PhET simulations), generative AI is an arrival technology — it entered classrooms before pedagogical evidence could form. The framework identifies where existing change models break down and derives actionable design implications for leading change under genuine uncertainty.

Central insight: "Institutions cannot wait for best practices, but cannot responsibly scale unjustified innovations. Neither banning nor uncritical embrace is tenable."

Six Dimensions of Reconsideration

Tools Dimension

1. Evidence Base: Prior models assumed reform begins with evidence-based tools; AI arrived without pedagogical evidence. Design implication: privilege humble, local inquiries — document what faculty are trying, share cautiously, avoid overclaiming generalizability.

2. Rate of Change: Prior models assumed stable tools; AI evolves on timescales of months. Design implication: organize reform around pedagogical approaches rather than specific AI tools, which may be obsolete before evidence accumulates.

3. Scope: Prior models targeted bounded interventions; AI's impact is broad and systemic. Design implication: adopt a systemic view — coordinate across courses and departments rather than responding piecemeal.

People Dimensions

4. Faculty: Prior models positioned faculty as adopters of proven practices; in the AI era, faculty must become inquiry-driven experimenters navigating genuine uncertainty. This connects to the faculty-development-genai finding that faculty are "pragmatic realists" navigating competing priorities with scarce resources.

5. Change Agents: Prior models cast change agents as disseminators of best practices; when best practices don't exist, they must become facilitators of collective inquiry. This reframes Centers for Teaching and Learning from trainers to community organizers — a transformation anticipated by universities-ai-era-rethinking.

6. Students: Prior models treated students as recipients; in the AI era, students are often ahead of faculty in AI use and must be engaged as partners in reform. This aligns with broader calls in student-experience and ai-literacy for student agency in AI-era education.

Design Implications

1. Privilege humble and local inquiries — document and share local experiments rather than scaling unproven practices 2. Organize around pedagogical approaches — anchor reform in durable pedagogical principles, not ephemeral tools 3. Reposition change agents as inquiry facilitators — build faculty learning communities around shared questions 4. Engage students as partners — leverage their AI experience in co-creating institutional responses 5. Build capacity for continuous adaptation — design for ongoing change, not one-time adoption

Application: Physics Department Workshop Series

The framework was piloted through a faculty workshop series in a university physics department. Rather than prescribing AI tools, workshops facilitated collective inquiry around pedagogical goals, engaged participants as co-investigators, and organized around teaching approaches rather than specific technologies. This connects to the departmental-level work described in stem-education and the workshop-based models in faculty-development-genai.

Connections to Wiki

Open Questions

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