If you teach or support entrepreneurs, you’ve seen how AI is changing how people learn, build, and test ideas. But how well is entrepreneurship education keeping up?
As part of my PhD in organizational psychology—and building on my experience teaching entrepreneurship and working with accelerators—I recently conducted a literature review on AI in entrepreneurship education. Using AI to help screen and classify over 850 articles, I then conducted full-text review and coding of 54 empirical studies—focusing on how curriculum, learning design, and entrepreneurial theory intersect in AI-enhanced entrepreneurship education.
What emerged was a clear pattern. While AI tools are being adopted widely in EE, they’re mostly reinforcing planning-heavy, predictive approaches—think AI-generated business model canvas and market research. There’s far less attention on approaches grounded in uncertainty, iteration, and adaptation. That’s where I believe the next frontier lies.
In response, I’ve been developed an AI-powered training tool that draws from the review’s findings and core entrepreneurship theories (Blank, 2013; Sarasvathy, 2001; Camuffo et al., 2024). The tool guides aspiring entrepreneurs through scaffolded, stage-based decision-making. It simulates real-world tests, offers AI-generated feedback, and—simulations of hypotheses testing. This tool helps ensure AI isn’t replacing the development of judgment, and instead is used to learn more and build faster with structure that supports their growth.
Check out the audio summary to understand the review’s findings and what we still need to learn about AI in entrepreneurship education.
Share this post