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All Journal Journal of Innovative Technologies in Learning and Education
YUNDONG WU
Dongshin University
Computer Science & IT Education Social Sciences Other

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AI-ENABLED INNOVATION AND ENTREPRENEURSHIP COURSES IN HIGHER EDUCATION: RESEARCH HOTSPOTS, EVOLUTIONARY TRAJECTORIES, AND EMERGING FRONTIERS YUNDONG WU
Journal of Innovative Technologies in Learning and Education Vol. 3 No. 2 (2026): Journal of Innovative Technologies in Learning and Education - September 2026
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/erhfvg60

Abstract

Artificial intelligence (AI), generative AI (GenAI), large language models, and intelligent agents are transforming university innovation and entrepreneurship education. These tools support opportunity recognition, market analysis, prototyping, pitch preparation, and feedback, yet raise critical concerns regarding evidence, authorship, data governance, and academic integrity. Although research in both fields has expanded, their intersection remains fragmented.  This article presents a CiteSpace-informed bibliometric synthesis examining AI-enabled innovation and entrepreneurship courses. A traceable core corpus of 48 international and policy sources was analyzed across key domains, mapping keyword co-occurrences, conceptual clusters, temporal trajectories, and emerging frontiers.  Five major research hotspots were identified: AI as an innovation collaborator; integration of AI literacy with entrepreneurial competence; authentic project-based learning; assessment and academic integrity; and ethical and governance boundaries. The field has evolved across four stages: tool introduction, pedagogical integration, curriculum reconstruction, and governance deepening. Emerging frontiers highlight course-specific intelligent agents, human-AI-teacher-enterprise collaboration, evidence-centered assessment, authentic user validation, and institutional responsible-use policies.  The article outlines a curriculum reform framework structured around problem chains, intelligent-agent scaffolding, learning-evidence portfolios, and risk-based governance. As an exploratory synthesis of a core corpus, findings offer a conceptual blueprint rather than an absolute measure of overall publication volume.
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