AI evidence extraction

Generative AI in Entrepreneurship Education: Enhancing Faculty's Instructional Design and Pedagogical Capacities

Meifang Yang, Hongyi Huo · Journal of Advances in Social Sciences · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.65192/npsp9s70

Methodology & findings

Study design

Conceptual literature review integrating recent literature (2022-2025) to develop a theoretical framework

Primary method

None—this is a narrative literature review with theoretical framework development

Main result

The study found that "GenAI enhances faculty capacities across instructional design (course development, case creation, activity design, assessment) and pedagogical competencies (innovative teaching, personalization, reflective practice)," while emphasizing that "realizing these benefits requires integration with professional judgment, as quality concerns—including fabricated citations and context adaptation needs—underscore the indispensable role of faculty expertise."

Reports effect sizes.

Research paradigm

Interpretivist/constructivist

Author conclusions

The authors conclude that "GenAI should be reconceptualized as a capacity development mediator rather than merely an efficiency tool." They further state that "this framework has significant implications for teacher education programs and institutional policies governing AI integration in entrepreneurship education." The study identifies "Four key contributions: (1) a dual-dimensional framework for GenAI-mediated capacity development; (2) identification of four distinct faculty adoption profiles requiring differentiated support; (3) critical challenges spanning academic integrity, ethics, digital equity, and training deficits; and (4) evidence-based multi-level intervention recommendations."

Risk of bias

Selection bias in literature review (narrative rather than systematic); Potential publication bias (only recent literature 2022-2025 included); Author subjectivity in framework development without systematic quality assessment; Limited empirical validation of proposed framework; Selection bias in literature reviewed (2022-2025 timeframe may miss earlier foundational work); Publication bias in favor of positive GenAI outcomes in early experimental studies; Potential confirmation bias in conceptual framework development

Limitations

The authors note that "while early experimental studies suggest potential time savings, realizing these benefits requires integration with professional judgment, as quality concerns—including fabricated citations and context adaptation needs—underscore the indispensable role of faculty expertise." Additionally, "critical challenges spanning academic integrity, ethics, digital equity, and training deficits" are identified as limiting factors in GenAI implementation.

Open questions raised

Need for empirical validation of the proposed dual-dimensional framework
Insufficient research on faculty adoption profiles and differentiated support strategies
Limited evidence on effective interventions addressing academic integrity and ethics concerns
Gap in understanding digital equity implications of GenAI in entrepreneurship education
Need for comprehensive faculty training programs
Need for systematic empirical validation of the proposed dual-dimensional framework

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