Determining AI-Based Learning Adoption Model for Students in Entrepreneurship Education: A Design Thinking Approach - Cep Abdul Baasith Wahpiyudin; Sabda Alam Muhammadan; Riska Amalia; Adelia Chrisanta; Asep Taryana (Department of Family and Consumer Sciences, Faculty of Social and Human Ecology, IPB University, 2025)
Background: Student interest in entrepreneurial pursuits remains low, despite the significant contributions of entrepreneurship to economic growth. Purpose: This study investigates the factors influencing IPB students' interest in adopting AI-based entrepreneurship learning through the lens of design thinking, emphasizing the role of communication methods and their impact on motivation and attitudes. Methods: This study adopts a mixed-method design, combining quantitative and qualitative approaches. Quantitative data were collected via an online survey from 173 IPB students, with 166 valid responses after data cleaning. Quantitative analysis was conducted using descriptive statistics (SPSS 25) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The qualitative aspect involved a SCAMPER analysis within the design thinking framework to explore AI integration in entrepreneurship education. The PICOS framework was applied to explore the factors influencing AI adoption in higher education comprehensively. This mixed-method approach provides a holistic understanding of AI adoption in educational contexts. Findings: Results indicate that learning motivation significantly affects students' intentions to engage with AI-based systems, positively impacting attitudes toward AI. Perceived ease of use also positively influences learning motivation and perceived usefulness, although perceived usefulness does not directly impact learning motivation. Additionally, interpersonal interactions and mass media positively influence attitudes and perceived usefulness, while awareness does not have a significant effect. Conclusion: Expanding AI adoption in entrepreneurship education requires strategic communication, mainly focusing on Design Thinking’s empathize phase to understand student challenges. By iteratively proposing AI tools through the prototype phase, institutions can develop user-friendly, engaging solutions tailored to student needs, fostering higher adoption and engagement in entrepreneurship learning. Research implication: These insights suggest that targeted communication strategies, including design thinking principles, can support broader AI adoption, enhance students’ entrepreneurial learning experiences, and foster a new generation of tech-savvy entrepreneurs.