AI for Entrepreneurial Learning

Entrepreneurial learning concerns acting on opportunities to create value for others. That value can be social, cultural, or financial; the activity need not produce a start-up. The European Commission’s EntreComp framework organises competencies around ideas and opportunities, resources, and action.¹ This guide proposes an AI-supported learning activity using that structure. It does not claim that the activity or AI use has been validated by EntreComp.

AI can propose alternatives, question assumptions, or rehearse a stakeholder conversation. Students must still investigate the need, judge the evidence, decide what to try, and take responsibility for their proposal.

Begin with ideas students can explain

Ask students to identify a problem from observation or approved course material and generate several ideas before consulting AI. Then use AI or peer critique to expose assumptions and broaden the comparison. Retain the initial ideas so students can explain what changed and why.

A short-story experiment found higher judged creativity with AI-generated ideas alongside greater similarity across the resulting stories. That result concerns that particular writing task, not an established effect on entrepreneurial projects.² Treat idea diversity as something to inspect locally, rather than assuming AI always broadens or narrows it.

Worked project: a campus repair service

The setting and figures below are fictional teaching material. A student team has a €200 prototype budget and two weeks to investigate whether a repair service could create value on campus. No demand, willingness to pay, repair skills, premises, or permission to operate has been established.

Learning outcome: justify an opportunity using evidence, compare feasible responses, and revise a decision when an important assumption is challenged.

1. Map the opportunity and alternatives

Students describe the problem and compare a repair event, a referral directory, and a tool-sharing scheme. They can ask AI for objections, but must explain whose need each option serves and what evidence is absent. Include people who might not benefit or could face participation barriers.

2. Make assumptions testable

Plan investigation with the educator before contacting people or collecting information. Participation must be voluntary; collect the minimum data needed and follow institutional requirements. Where live investigation is unsuitable, provide an educator-prepared evidence pack and assess reasoning within its limits.

3. Rehearse without inventing customers

AI may play a sceptical stakeholder in a simulation, using only a supplied role card. Students can practise open questions and notice leading wording. Record simulated answers as fictional rehearsal material. They are not interviews, testimonials, a market sample, or evidence of demand.

Help us challenge a proposal for a classroom project.
Our proposal, constraints, and evidence with source identifiers: [material]

Identify three consequential assumptions. For each, explain what evidence is
missing and propose a feasible way we could investigate it with educator
approval. Do not invent customers, quotations, demand, prices, permissions,
market size, or research. Distinguish evidence in the pack from hypothetical
possibilities. Ask one challenge question and wait for our response.

4. Choose a bounded experiment

Students propose the smallest appropriate test that fits the budget, time, permissions, and learning outcome. A paper prototype or comparison of service routes may be sufficient. Do not perform repairs, purchase supplies, or deploy a service simply because a generated plan recommends it.

Before the test, name a decision criterion: what evidence would support proceeding, changing direction, or stopping? Avoid defining success only after results arrive. Any numerical threshold is a course-specific decision to justify, not a universal rule about viability.

5. Revise the recommendation

Suppose an educator-supplied update says the proposed event needs a qualified supervisor who is unavailable during the project. The team should reconsider the event, compare a referral prototype, or pause. Inventing approval or treating an AI-generated safety plan as authorisation is not an acceptable response.

Assess the decisions and evidence

Use a short portfolio containing the initial ideas, an assumption table, sources or authorised observations, a bounded test plan, and a recommendation with one consequential revision. Require a brief AI disclosure when applicable, not full conversations. A highly polished pitch cannot compensate for invented evidence.

For independent transfer, supply a new constraint and ask each student to explain its effect on the recommendation. Use the professional-case assessment example to adapt a brief and rubric. Assess equivalent evidence for students using peer critique or a shared AI artefact instead of an individual tool.

Use the workflow evaluation guide to compare this design with the existing project approach, including idea diversity, reasoning, participation, and total workload. Do not present a fictional project as an implementation case report.

References & Footnotes