Towards an AI-Augmented Graduate Model for Entrepreneurship Education: Connecting Knowledge, Innovation, and Venture Ecosystems

Jiaqi Gong (), James Geyer, Dwight W. Lewis, Hee Yun Lee and Karri Holley
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Jiaqi Gong: Alabama Center for the Advancement of Artificial Intelligence, College of Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
James Geyer: Institute of Rural Health Research, College of Community Health Sciences, University of Alabama, Tuscaloosa, AL 35487, USA
Dwight W. Lewis: Department of Management, College of Business, University of Alabama, Tuscaloosa, AL 35487, USA
Hee Yun Lee: School of Social Work, University of Georgia, Athens, GA 30602, USA
Karri Holley: Department of Higher Education Administration, College of Education, University of Alabama, Tuscaloosa, AL 35487, USA

Administrative Sciences, 2026, vol. 16, issue 1, 1-32

Abstract: Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts.

Keywords: AI-augmented entrepreneurship; venture creation and innovation; entrepreneurial ecosystems; graduate education models; technology-enhanced pedagogy (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2026
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