Integrating AI-Based Modeling and Data Analysis into Entrepreneurship Education: Insights from a Multi-Stakeholder FGD Approach Glory Aguzman1, a), Johan2, b, Desman Hidayat3, c, Anggha Dipa Pratama4, d, Lorio Purnomo5, e, Pantri Heriyati6, f
Binus University
Abstract
There are now more options for entrepreneurship education due to the growing importance of artificial intelligence (AI) in data-driven innovation and decision-making. However, particularly in venture contexts related to STEM, traditional entrepreneurship courses sometimes lack integration with computational modelling and simulation tools. To close this gap, this study explores how entrepreneurship curriculum might incorporate AI-supported modelling and data analysis tools to improve student venture validation and prototyping. Three Focus Group Discussions (FGDs) were held with industry practitioners, startup incubator coaches, and entrepreneurship lecturers using a qualitative exploratory approach. The goal of these talks was to jointly create an interdisciplinary course called Venture Creation that incorporates data science and artificial intelligence techniques that were modified from experimental physics. According to the findings, there is widespread support for integrating predictive analytics, AI-based simulations, and prototyping tools into entrepreneurial education. The advantages of giving students the ability to test hypotheses through iteration, make well-informed, evidence-based decisions, and participate in hands-on, real-world venture development were underlined by stakeholders. Interdisciplinary assessment, faculty readiness, and departmental curriculum collaboration are among the issues mentioned. In summary, the study shows that by connecting theoretical knowledge with real-world application, incorporating AI technologies into entrepreneurship education improves the calibre of venture formation. To successfully integrate AI as a cognitive and technological partner in entrepreneurship learning, the suggested curriculum emphasises the necessity of systemic transformation, including faculty development, interdisciplinary rubrics, and modular course design.
