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, Educational Policy AI).

Institutional Change Framework for AI

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 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.

Connections to Wiki

Open Questions

Connected Concepts

Connected Articles

Citation

Perl-Nussbaum, D., & Finkelstein, N. D. (2026). A Framework for Institutional Change in the Age of AI. arXiv:2605.12757.