Reflecting on the Competence of Entrepreneurs using Entrecomp, AI and Narratives People Know and Love

Penaluna, Kathryn and Ranson, Paul (2024) Reflecting on the Competence of Entrepreneurs using Entrecomp, AI and Narratives People Know and Love. Innovation and Education. ISSN 2524-8502 (In Press)

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Abstract

The significance of entrepreneurship as a driver of economic sustainability is globally recognised, leading to increased political engagement to foster entrepreneurial skills in higher education. This paper endeavours to mirror the contemporary landscape of entrepreneurial education amidst the rising prominence of generative AI and explores the integration of entrepreneurship education, storytelling, and AI, proposing a conceptual framework that leverages the rising prominence of generative AI. Two premises guide this study: first, that Case Method Teaching (CMT), which uses storytelling for student reflection on diverse challenges, can be enhanced with AI- Generated Entrepreneurial Narratives; second, that the demanding nature of curating complex entrepreneurial cases can be alleviated with AI. By analysing narratives from celebrated texts, AI can enhance entrepreneurial competencies outlined by the European Commission’s EntreComp framework. This approach enables educators to create engaging lessons aligned with developing key competencies, enhancing reflective learning environments and educators' and learners understanding of generative AI.

Item Type:	Article
Uncontrolled Keywords:	Entrepreneurship Education; Generative Artificial Intelligence; Case Method Teaching; EntreComp Framework
Subjects:	L Education > LB Theory and practice of education L Education > LB Theory and practice of education > LB2300 Higher Education L Education > LB Theory and practice of education > LB2361 Curriculum L Education > LC Special aspects of education
Divisions:	Research Innovation and Enterprise Services
Depositing User:	Kathryn Penaluna
Date Deposited:	10 Dec 2024 14:04
Last Modified:	10 Dec 2024 14:07
URI:	https://repository.uwtsd.ac.uk/id/eprint/3281

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