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Data · dataset · 2026

Constraint-driven AI coaching in academic entrepreneurship education: Evidence on entrepreneurial readiness and orientation under responsible design

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<p dir="ltr">The adoption of a Generative AI (<a href="" target="_blank">Gen-AI)</a> transformation can yield substantial benefits, but it can also create negative effects for end users, including bias, privacy abuses, and reduced engagement with human faculties.

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Scholars also highlight evidence that frequent use of large language models may be associated with cognitive decline and reduced performance, particularly when they mediate judgment and decision-making.

This concern becomes particularly acute in university-wide entrepreneurship education, where Gen-AI can scale personalised guidance but also encourage cognitive offloading and premature convergence. The central question we address in this contribution is how universities can deploy Gen-AI to augment entrepreneurial learning while preserving learner agency and keeping responsibility for reasoning, judgement, and ethics with human learners rather than with the system.</p><p dir="ltr">Entrepreneurship education can unintentionally privilege validation over transformation, thereby encouraging incremental refinement of ideas rather than deeper reframing that can generate novel value propositions [5].

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Gen-AI is primarily used to support the validation phase by accelerating and improving the productivity of crucial steps such as persona identification and prototyping [6]. Instead, there are limited attempts to leverage Gen-AI to enhance human creativity and support the generation of better ideas and their transformation into testable business concepts.</p><p dir="ltr">Drawing on a design-driven view of entrepreneurship in which opportunities are treated as artefacts to be shaped [1], we conceptualise ideation as a process that must engage uncertainty rather than bypass it.

In this framing, intellectual agility refers to the ability to move between divergent exploration and convergent decision-making under uncertainty, while retaining accountability for choices, assumptions, and consequences [7].</p>

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Artificial intelligence 72%
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