Artificial intelligence use, entrepreneurial mindset, and perceived work-based learning readiness in higher education

Purpose This study examines the relationship between artificial intelligence (AI) use in learning, students' entrepreneurial mindset, and perceived work-based learning readiness in higher education. It investigates whether AI use in learning is associated with entrepreneurial mindset and perceived work-based learning readiness, and whether entrepreneurial mindset is associated with perceived work-based learning readiness. Design/methodology/approach A cross-sectional quantitative survey was conducted with 300 undergraduate students from business and entrepreneurship-related programmes in Indonesian higher education institutions. The respondents had prior experience using AI tools for learning purposes. Data were analysed using Partial Least Squares Structural Equation Modelling to assess the measurement model and structural relationships among the constructs. Findings The findings indicate positive associations between AI use in learning, entrepreneurial mindset, and perceived work-based learning readiness. AI use in learning is positively associated with entrepreneurial mindset and perceived work-based learning readiness, while entrepreneurial mindset is also positively associated with perceived work-based learning readiness. These findings should be interpreted as evidence of self-reported perceptions rather than actual workplace performance or behavioural capability development. Research limitations/implications This study has several limitations. First, the use of cross-sectional self-reported survey data limits causal interpretation and may be affected by common method bias or social desirability. Second, the study measures perceived work-based learning readiness rather than actual workplace performance, internship outcomes, or employer-rated competence. Third, the sample is limited to students in business and entrepreneurship-related programmes, which may restrict generalisability. Future research should use longitudinal, mixed-method, or multi-source designs involving students, educators, employers, and workplace supervisors to examine how AI-assisted entrepreneurial learning influences actual work-based learning capability and graduate employability. Practical implications The findings suggest that higher education institutions should integrate AI-assisted learning into entrepreneurship curricula in a responsible and pedagogically guided manner. Educators can use AI tools to support idea generation, market analysis, problem-solving, project development, and internship preparation. However, AI use should be accompanied by clear ethical guidelines, AI literacy training, and activities that strengthen independent judgement, creativity, and critical thinking. Institutions should also connect AI-supported entrepreneurial learning with work-based learning activities, such as industry projects, capstone projects, employability training, and workplace simulations. Social implications This study highlights the social importance of preparing students to engage with AI responsibly in entrepreneurial and work-based learning contexts. Responsible AI-supported learning may help students develop digital confidence, entrepreneurial awareness, and readiness for technology-driven workplaces. However, the findings also suggest the need to prevent overdependence on AI and ensure that students continue to develop creativity, ethical judgement, and independent problem-solving. By promoting responsible AI literacy, higher education institutions can support more inclusive, adaptive, and future-oriented graduate preparation. Originality/value This study contributes by positioning entrepreneurial mindset as an important pedagogical and psychological construct in understanding how AI-assisted learning relates to students' perceived readiness for work-based learning. The study extends discussions on AI in higher education by connecting AI-assisted learning with entrepreneurship education and work-based learning preparation, while acknowledging the limitations of perceptual and cross-sectional evidence.