Research Article
AI Intrapreneurship: Educators as Frontline Innovators in the Age of Generative AI
Synthesis: Zimmer argues that Generative AI disrupted higher education faster than institutions could respond, and that the answer is not top-down strategy but educator-led "AI intrapreneurship." Drawing on intrapreneurship theory (Pinchot, 1985; Neessen et al., 2019), he defines it across three interdependent modes: building AI-powered pedagogical tools, building AI-resilient Assessment and learning strategies, and reclaiming bandwidth by using AI to cut administrative burden. The case rests on a documented gap — 72% of surveyed US instructors have used generative AI for at least one instructional purpose, yet only 14% feel confident using it for teaching — and on his claim that AI both created the disruption and lowered the barrier to answering it, through Vibe Coding and no-code building. He maps concrete use cases and organizational enablers, then concludes that universities must learn to recognize, support and scale faculty-built innovation or "risk being innovated around by their own faculty." Students and employers, he warns, may otherwise stop treating the degree as evidence of learning.
Key Findings
- AI intrapreneurship describes educators who take hands-on responsibility for building AI-powered teaching solutions, building AI-resilient solutions that "ensure authentic, verifiable student learning," and reclaiming bandwidth by reducing administrative burden.
- Adoption outruns confidence: 72% of surveyed US instructors have used generative AI instructionally, yet only 14% feel confident using it for teaching; faculty adoption rose from 24% in 2023 to 49% in 2025.
- Institutional response lags the disruption: 57% of institutions treat AI as a strategic priority, yet only 22% had an institution-wide AI strategy and 39% formal acceptable use policies.
- The author argues for redesign over detection, pointing to Australia's TEQSA Two-Lane Approach and the AI Assessment Scale's "five-level framework for calibrating AI use."
- Vibe coding lowers the barrier: Karpathy coined the term in February 2025, and the author — who says he does not know how to code — used Claude Code to build a link-checker for 321 course links.
- Hornsby et al.'s (2002) Corporate Entrepreneurship Assessment Instrument names five organizational enablers, and Adobe's Kickbox gave each employee a kit with $1,000 in seed funding and "no approval requirement."
Why assessment, not detection, forced the issue
The pressure on faculty, in Zimmer's account, came from assessment rather than AI. A 2025 survey of over 1,000 UK undergraduates found 92% used AI tools in some form, up from 66% a year earlier, and 88% used generative AI for assessments; a Digital Education Council survey of nearly 3,900 students across 16 countries reported 86% usage. Asynchronous written work, he argues, can be completed "almost instantly": in fall 2025 he used OpenAI's agentic Atlas browser to finish an entire assignment from his own composition course. Detection — 14 tools evaluated in 2023 ranged from 33% to 81% accuracy, with false positives of 0% to 32%. General-purpose models complete most asynchronous written assessments.
Three modes and one behavioral core
Zimmer defines intrapreneurship as behaving entrepreneurially within an existing organization (Pinchot, 1985), distinct from corporate entrepreneurship (Sharma & Chrisman, 1999): a university launching an AI task force is the latter — "when a professor builds a custom chatbot for her students on a weekend, that is intrapreneurship." The modes interlock: reclaiming bandwidth creates time for the other two, and building competence with AI informs resilience design, since faculty who know what AI can do can better design assessments it cannot shortcut. Neessen et al.'s (2019) dimensions — innovativeness, proactiveness, risk-taking, opportunity recognition and networking — describe what educator-innovators already do: spotting a compromised discussion board, redesigning it before departmental guidance arrives, sharing the template. He calls the modes connected responses to one disruption.
Vibe coding and the conditions that decide whether it lasts
Vibe coding — Karpathy's February 2025 coinage for building software through natural-language prompts — drops the barrier from years of programming to describing what you need. MIT Sloan's no-code Stack AI let faculty build course chatbots and simulations, and the author, unable to code, used Claude Code for a 321-link checker. Prototypes are not deployments: LMS integration, FERPA compliance, Accessibility and maintenance need institutional partnership, and Veracode's 2025 analysis found only 55% of AI-generated code secure. The decisive enablers are organizational — work discretion, rewards, and time availability, the last "most obviously in deficit in academic settings" and undercut by promotion and tenure systems. Without protected time, the intrapreneurial impulse "risks getting snuffed out early."
What this means for practice
- Treat faculty-built AI tools as innovations to support, not shadow IT to suppress, with lightweight prototyping and no early approval gates.
- Count assessment redesign as scholarly work in promotion and tenure, the enabler Mode 2 depends on.
- Design two-lane assessment: secure verification for foundational competency, transparent AI-engaged work for professional capability.
- Protect faculty time; three hours a week saved on compliance can fund redesign and tool building.
Limitations
- The three-mode construct is conceptual; the author presents use cases and calls for empirical testing.
- The AI-inflation figure cited — roughly 30 percentage points, a Cohen's d of 1.51 — comes from a preprint needing confirmation.
- Evidence leans on first-person anecdotes, not systematic data.
- Non-tenure-track faculty, instructional designers and community college instructors are largely absent from that literature.
Connected Concepts
Connected Articles
- A Guiding Framework for K-12 Teachers in Creating AI-powered Learning Technologies through Vibe Coding — vibe coding with K-12 teachers.
- Beyond Detection: How Students Use—and Hide—AI in Online Assessments and What Authentic Tasks Can Do About It — redesigning online assessment beyond detection.
- The AI Challenge: How college faculty assess the present and future of higher education in the age of AI — faculty survey evidence on AI.
- From fear to innovation: A case study of transformative faculty development for ethical AI integration in higher education — faculty development for ethical AI.
Citation
Zimmer, M. G. (2026). AI Intrapreneurship: Educators as Frontline Innovators in the Age of Generative AI. Journal of Instructional Design and Technology, 1(2), 45-54.