AI in Entrepreneurship Education: Interdisciplinary Action on the Frontier

Twenty-two Babson faculty contribute eighteen teaching innovations, organized around the five practices of entrepreneurship education. Edward Elgar Publishing, 2026 (In Press).

Erik Noyes, Jonathan Sims, Kristi Girdharry, Davit Khachatryan, and Candida Brush · Babson College

Dawn of a New Era

When the internet emerged as a commercial force in the mid-1990s, organizations faced a choice: adapt or risk irrelevance. Some thrived by building around digital connectivity; others waited, certain the disruption would pass. It did not. A similar fork now confronts entrepreneurship educators.

By several measures, the shift to artificial intelligence (AI) is arriving faster and reaching further than ever before, and the implications are still emerging. To put this in perspective, consider these findings: A 2025 United Nations Educational, Scientific and Cultural Organization (UNESCO) survey shows that nearly two-thirds of higher education institutions have or are developing guidance on the use of AI (UNESCO, 2025). A 2024 Ellucian survey of higher education administrators shows 93% of respondents expect to expand their use of AI over the next two years (Ellucian, 2024), while a 2025 Digital Education Council global survey shows 61% of faculty are using AI in their teaching, although most of them minimally (Digital Education Council, 2025). Further, a 2025 survey from the Higher Education Policy Institute (HEPI) shows that approximately 88% of students are using generative-AI (GenAI) tools for assessments (Freeman, 2025).

Shepherd and Majchrzak (2022) propose that AI combined with entrepreneurship represents a “super tool” capable of reshaping how ventures are imagined, launched, and scaled. Obschonka et al. (2025) characterize the current moment as a “Wild West” defined by “limitless horizons, massive inflows of people, ideas, and resources, as well as comparatively few established norms” (p. 621). Both assessments ring true. The potential is real; so is the disorientation. And our students—who arrive in classrooms already experimenting with GenAI before most faculty have encountered it—are watching to see whether we engage or retreat.

We know this from our own classrooms. When we began asking students to use AI as part of entrepreneurship exercises, the results were not what we expected. Some students produced work of startling sophistication—business models pressure-tested against AI-simulated competitors, pitch decks refined through dozens of iterative prompts. Others submitted AI outputs without interrogation or revision, mistaking fluent text for finished thinking. Both responses taught us something essential: The tool amplifies whatever the student brings to it—curiosity or passivity, rigor or shortcut. The irony is that AI can be an empowering force multiplier for the curious student who is intent on using it to augment their own work—and a thin substitute for curiosity for the student who uses it to replace their own work.

Those uneven results were shaped partly by which platforms students used, and that points to a distinction worth drawing early. AI products and services will come and go, while the capabilities they enable—text generation, image synthesis, code production, and multimodal sensemaking—will continue to expand.

Noy and Zhang (2023) document measurable productivity gains among workers using GenAI, but productivity alone does not capture what matters for entrepreneurship educators. Short and Short (2023) show that prompt engineering is itself a form of entrepreneurial rhetoric—the craft of refining language iteratively to explore what one aims to say. Students are not merely gaining productivity tools; they are practicing a new language of strategic communication. These are capabilities, not features of any particular product—and this book addresses capabilities rather than ever-changing tools.

With that grounding, this book is not an argument that every educator must adopt AI. It is an argument that every educator should understand it. Faculty who choose not to use AI in their courses are making a defensible choice, but that choice is informed only if it rests on a clear understanding of what students can now do, what capabilities are emerging, and how entrepreneurship itself is changing. For those who choose to adopt AI, this book will help in the transition by providing tools, exercises, and activities to apply the technology effectively.

This book is rooted in practice theory (cf. Rouse, 2007; Bourdieu, 1990; Pickering, 1992; Giddens, 1984) “and the belief that particular kinds of learning activities can ‘generate richer understanding about practice, but from and through practice, not on behalf of it’ ” (Billett, 2010, p. 29, as cited in Neck et al., 2014; Neck, et al., 2021). Following the lead of Neck et al. in their Teaching Entrepreneurship volumes (2014; 2021), we organize this work around five practices identified as core to entrepreneurship education: play, empathy, creation, experimentation, and reflection.

Practice as a pedagogy is by definition multidisciplinary, “the enactment of the kinds of activities and interactions that constitutes the occupation” (Billett, 2010, p. 22, as cited in Neck et al., 2014; 2021). As such, we invite readers from every corner of the university to see themselves in this book. It is written for educators across disciplines, not only those who teach entrepreneurship. Entrepreneurship draws on economics, psychology, sociology, design, and communication; people start businesses in every discipline, from theater to health care to engineering (Yi & Duval-Couetil, 2021). AI is transforming multiple fields and professions simultaneously, as computer scientists build new systems, designers rethink prototyping, and humanists reconsider authorship by revisiting enduring questions about originality, voice, narrative construction, interpretive authority, and what it means to “author” something in collaboration with a machine. All face far-reaching questions about human-AI collaboration. Both AI and entrepreneurship are inherently interdisciplinary, and their intersection only deepens the need for perspectives that no single field can supply.

In this chapter we explore how AI has changed the practice of entrepreneurship across disciplines, detailing prospects and challenges our graduates will face. We then examine how AI has profoundly disrupted teaching and learning. Finally, we focus on the opportunities this moment presents for those who teach entrepreneurship through various disciplinary lenses.

Excerpts

The New Entrepreneurial Landscape

New venture creation is being transformed in parallel. AI lowers barriers to starting and scaling in unprecedented ways (Chalmers et al., 2021; Davenport & Noyes, 2025). An entrepreneur with access to GenAI can build a functional prototype, draft a business plan, conduct preliminary market research, and create marketing materials in days rather than months (Short & Short, 2023). With AI agents, the minimum viable team has shrunk to one (Shepherd & Majchrzak, 2022). Creative destruction, the process Schumpeter (1942) placed at the center of capitalist dynamism, is accelerating as AI compresses the cycles through which industries are disrupted and new opportunities emerge (Norbäck & Persson, 2024). Perhaps most consequentially, AI serves as what Davidsson et al. (2020) call an “external enabler”—an environmental condition that creates new entrepreneurial opportunities by allowing founders to scan vast amounts of information and recognize previously imperceptible patterns.

How AI Changes Teaching and Learning

The discipline of entrepreneurship education is itself in transition—from teaching students how to unearth customer needs to teaching them how to iterate venture concepts and create expressive functioning prototypes (Vecchiarini & Somià, 2023). That shift was underway before AI arrived, but AI has accelerated it decisively. If a chief pedagogical goal of entrepreneurship education is iteration and adaptation, then AI is not a disruption to the curriculum. It is the curriculum's natural next step.

How AI Enhances the Five Practices of Teaching Entrepreneurship

AI does not displace these practices—it amplifies them. The question is not “How do I use ChatGPT in my course?” but “How does AI change what is possible within each practice?” AI phenomena are much broader than current-day large language models (LLMs), and the five practices provide a durable architecture for that inquiry, one that will outlive any particular AI tool.

Risks, Ethics, and the Work Ahead

Responsible engagement with AI requires honesty about what can go wrong. There are risks of inaction, as students will use AI regardless, and educators who stay silent cede the interpretive ground. There are risks of moving too slowly, as institutions that delay fall further behind each semester. And there are risks of obsolescence, as curricula that ignores AI becomes disconnected from professional reality.

This Book

These commitments are animated by urgency. Students expect thoughtful engagement in AI from their instructors. Faculty who have a limited understanding of AI lose credibility; those who engage thoughtfully create opportunities for innovation both in the classroom and the market. A frontier is a space of possibility, and faculty who engage now, ethically and with open eyes, will shape the norms and possibilities other educators inherit.