The AI-Augmented Entrepreneurship Pedagogy Action Model (AI-EPAM): A Transformative Framework for Entrepreneurial Skill Development and Venture Success

Abstract

Entrepreneurship education in many emerging and digitally transforming economies continues to exhibit a persistent paradox of high entrepreneurial intention alongside weak venture sustainability. This study addresses this execution gap by proposing the AI-Augmented Entrepreneurship Pedagogy Action Model (AI-EPAM), a systemic pedagogical framework designed to support execution-oriented capability development. Using a design-based research approach, the study integrates three complementary empirical components, which include a curriculum analysis of 58 entrepreneurship-related courses across nine Thai universities, a macro-level analysis of entrepreneurial ecosystem patterns using Global Entrepreneurship Monitor data, and expert-based conceptual validation through a two-round Delphi study. The curriculum analysis reveals a dominant emphasis on theory-oriented and planning-focused instruction, with limited integration of experiential, data-informed, and AI-enabled learning. The ecosystem analysis identifies structural patterns in which high entrepreneurial participation coexists with weak innovation capability, limited digital readiness, and elevated fear of failure during venture execution. Expert validation underscores the importance of educator mediation, institutional readiness, and ethical governance in AI-augmented entrepreneurship education. Synthesizing these findings, AI-EPAM conceptualizes artificial intelligence not as a stand-alone instructional tool but as a mediated pedagogical co-agent within a triadic learning ecosystem comprising learners, educators, and AI systems. While the study does not empirically evaluate learning or venture outcomes, it offers a theoretically grounded and empirically informed framework that addresses execution-oriented learning gaps and provides a foundation for future research on AI-augmented pedagogy.