In a nutshell
As AI is changing how entrepreneurship fundamentally works, this paper offers a framework for bringing AI into entrepreneurship education through a familiar structure: learning about, for, and through entrepreneurship.
There’s no denying it: entrepreneurs can now use AI to analyse markets, generate ideas, communicate with customers, test assumptions, support decision-making, shape business models – all these things that used to require far more time, resources, or specialist expertise. So naturally, if AI is changing what entrepreneurs can do, it also changes what future entrepreneurs need to learn. Of course they still need to strengthen and cultivate the usual entrepreneurial muscles and mindset, but they also need to understand how AI works in entrepreneurial contexts, how to use it critically, and how to remain responsible for decisions made with its support.
This is the challenge Heather Bell and Robin Bell take up in their conceptual paper. They propose a framework for integrating AI into entrepreneurship education as part of entrepreneurial capability development, without asking educators to start from scratch. Instead, they build on a structure many in the field already know: learning about, for, and through entrepreneurship.
In simple terms, students can learn about entrepreneurship by developing conceptual understanding, for entrepreneurship by building skills and competences, and through entrepreneurship by engaging in entrepreneurial activity. Bell and Bell extend this familiar structure to AI, making the point that students do not need the same kind of AI learning at every stage. Sometimes they need to understand AI, sometimes they need to practise using it, and sometimes they need to work with it in more realistic entrepreneurial contexts, where judgement and accountability become part of the learning.
That starts with understanding how AI is changing entrepreneurial practice. The aim is not to turn every student into an AI specialist – which would be both unrealistic and mildly uncool – it is to build enough conceptual AI literacy for them to understand both the possibilities and the limits of these systems.
Publication year: May 2026
Authors: Heather Bell and Robin Bell
Institutions: Lycoming College, USA; Worcester Business School, University of Worcester, UK
Study type: Conceptual/theoretical paper
Sample Size: Not applicable
Research focus: Developing a framework for progressively integrating AI into entrepreneurship education.
Research Methodology: Conceptual and integrative synthesis drawing on the about–for–through tradition in entrepreneurship education, literature on AI in entrepreneurship education, and learning theory.
Main findings: The paper proposes the AI-Enabled Entrepreneurial Learning Progression Framework, which frames AI integration as a staged capability-development process. It moves from conceptual literacy and analytical understanding, to applied integration and evaluative judgement, and finally to entrepreneurial capability, identity formation, and responsible agency in AI-enabled venture contexts.
Citation: Bell, H., & Bell, R. (2026). Extending the about–for–through tradition for AI-enabled entrepreneurship education: the AI-enabled entrepreneurial learning progression framework. Entrepreneurship Education. Link
From there, students can begin using AI in entrepreneurial tasks. However, the learning does not come from producing a shiny AI-assisted output and calling it a day. It comes from learning how to evaluate what AI gives them. What assumptions is it making? What evidence is missing? Where might bias creep in? Does the output actually hold up, or does it just sound confident in that particular way machines have mastered suspiciously well?
The final step is using AI as part of more authentic entrepreneurial activity. This might happen in live projects, venture development, incubator settings, or externally engaged work. Here, AI becomes part of the environment students are acting within. It can suggest, generate, analyse, and summarise, but the responsibility for entrepreneurial decisions still rests with the person using it. The tool can help to steer, but it does not get to hold the driving licence.
That is where this framework becomes especially useful for educators. Before redesigning an assignment around whatever platform everyone is currently panicking about, educators can ask a more useful question: what do students need at this point in their learning? Do they need to understand how AI affects entrepreneurship? Do they need to practise using it critically? Or do they need to apply it in contexts where decisions, consequences, and responsibility matter?
The paper is theoretical, so the framework still needs to be tested in practice, but it does offer a helpful way into a fast-moving educational challenge. If AI is redefining the capabilities required of entrepreneurs, entrepreneurship education needs to respond with more than scattered tool use. Bell and Bell’s framework suggests one way to do that: by helping students understand AI, work with it critically, and take responsibility for what they create with it.
For more on AI-generated comics in entrepreneurship education, see An Unexpected Way To Use AI in Entrepreneurship Education
For more on the entrepreneurial method and learning under uncertainty, see Introducing the Entrepreneurial Method
For more on preparing students for radically different futures, see Teaching the Future Before It Arrives
For more on how educators apply design thinking in entrepreneurship education, see How Educators Apply Design Thinking to Entrepreneurship Education
