Developing Entrepreneurial Mindset Among Non-Business Majors Through Experiential Learning and AI Tools

Despite the proliferation of entrepreneurship education (EE) programs, conventional pedagogies often fall short in cultivating the critical cognitive and affective dimensions of the entrepreneurial mindset, particularly in areas such as opportunity recognition, creative problem-solving, and resilience. This study addresses this enduring pedagogical challenge by embedding experiential learning and generative artificial intelligence (AI) tools into an entrepreneurship education. Drawing upon Kolb’s Experiential Learning Theory and Biggs’ Constructive Alignment frameworks, the instructional design integrated immersive fieldwork, problem-based tasks, and iterative AI-supported learning experiences. Generative AI tools were strategically deployed to scaffold ideation, support inquiry, and enhance formative feedback throughout the entrepreneurial process. Using a mixed-methods approach, the study assessed the entrepreneurial mindset (EM) of 92 postgraduate students before and after the intervention. Partial Least Squares Structural Equation Modeling (PLS-SEM) confirmed EE explains 47.8% of the variance in EM. Quantitative findings reveal a statistically significant 35.48% increase in EM scores. Qualitative data, drawn from student reflections and thematic analyses, further underscored the perceived value of AI integration and real-world engagement in fostering entrepreneurial thinking, industry relevance, and innovation capacity. The findings contribute to the evolving discourse on entrepreneurship education by demonstrating the pedagogical value of integrating immersive, AI-augmented experiential learning in developing future-ready entrepreneurial competencies. By incorporating AI into the experiential learning framework, students were not only trained in research methods and entrepreneurial problem-solving but also in AI literacy, which is increasingly seen as an essential futuristic skill. This study makes a theoretical contribution by empirically validating how Kolb’s Experiential Learning Theory and Biggs’ Constructive Alignment Model can be enhanced through AI integration in entrepreneurship education. It offers actionable insights for educators and institutions seeking to modernize entrepreneurship programs. Our problem-based approach, incorporating immersive field trips, AI-assisted research tools (ChatGPT, DeepSeek), and authentic assessments, proved particularly effective in developing students’ critical thinking, opportunity recognition, and adaptive problem-solving skills.

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Publication Details

Journal
European Conference on Knowledge Management
Published
2025-08-29
DOI
https://doi.org/10.34190/eckm.26.1.3762
Primary Topic
Human Resource and Talent Management
Type
article
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article

Developing Entrepreneurial Mindset Among Non-Business Majors Through Experiential Learning and AI Tools

Fawad Ahmed, Ying Tuan Lo, Shu-Hsiang Chen
European Conference on Knowledge Management
Human Resource and Talent Management
article

Developing Entrepreneurial Mindset Among Non-Business Majors Through Experiential Learning and AI Tools

Fawad Ahmed, Ying Tuan Lo, Shu-Hsiang Chen
article en

Abstract

Despite the proliferation of entrepreneurship education (EE) programs, conventional pedagogies often fall short in cultivating the critical cognitive and affective dimensions of the entrepreneurial mindset, particularly in areas such as opportunity recognition, creative problem-solving, and resilience. This study addresses this enduring pedagogical challenge by embedding experiential learning and generative artificial intelligence (AI) tools into an entrepreneurship education. Drawing upon Kolb’s Experiential Learning Theory and Biggs’ Constructive Alignment frameworks, the instructional design integrated immersive fieldwork, problem-based tasks, and iterative AI-supported learning experiences. Generative AI tools were strategically deployed to scaffold ideation, support inquiry, and enhance formative feedback throughout the entrepreneurial process. Using a mixed-methods approach, the study assessed the entrepreneurial mindset (EM) of 92 postgraduate students before and after the intervention. Partial Least Squares Structural Equation Modeling (PLS-SEM) confirmed EE explains 47.8% of the variance in EM. Quantitative findings reveal a statistically significant 35.48% increase in EM scores. Qualitative data, drawn from student reflections and thematic analyses, further underscored the perceived value of AI integration and real-world engagement in fostering entrepreneurial thinking, industry relevance, and innovation capacity. The findings contribute to the evolving discourse on entrepreneurship education by demonstrating the pedagogical value of integrating immersive, AI-augmented experiential learning in developing future-ready entrepreneurial competencies. By incorporating AI into the experiential learning framework, students were not only trained in research methods and entrepreneurial problem-solving but also in AI literacy, which is increasingly seen as an essential futuristic skill. This study makes a theoretical contribution by empirically validating how Kolb’s Experiential Learning Theory and Biggs’ Constructive Alignment Model can be enhanced through AI integration in entrepreneurship education. It offers actionable insights for educators and institutions seeking to modernize entrepreneurship programs. Our problem-based approach, incorporating immersive field trips, AI-assisted research tools (ChatGPT, DeepSeek), and authentic assessments, proved particularly effective in developing students’ critical thinking, opportunity recognition, and adaptive problem-solving skills.

European Conference on Knowledge ManagementVol. 26(1)
Xi’an Jiaotong-Liverpool University (CN)
Openalex Percentile: Top 73%
Human Resource and Talent Management
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