### Introduction

The present study aimed to develop a comprehensive model for the application of artificial intelligence (AI) in entrepreneurial skills education at Kermanshah University of Medical Sciences. Given the rapid pace of technological advancements and the increasing complexity of educational systems, leveraging the potential of AI to enhance the quality of entrepreneurship education has become an undeniable necessity. With its capabilities in analyzing educational data, simulating real-world environments, and designing personalized learning pathways, AI can transform the learning process from a traditional approach into a dynamic, interactive, and creative experience. Medical universities, owing to their dual educational and innovation-oriented roles, likewise need to harness this technology to cultivate entrepreneurial and capable students equipped to operate in interdisciplinary domains. Accordingly, this study sought to identify the dimensions, components, and consequences of AI application and to develop a context-specific model for entrepreneurial skills education.

### Methodology

In terms of its purpose, this study is developmental, and in terms of its approach, it adopts a qualitative methodology. Data were collected through semi-structured interviews with 16 participants, including information technology experts, entrepreneurs, faculty members, and students at Kermanshah University of Medical Sciences. Participants were selected through purposive sampling using a snowball sampling technique. Data analysis was conducted based on the Strauss and Corbin grounded theory approach through three stages of open, axial, and selective coding. To ensure the credibility of the data, member checking was employed, while inter-coder agreement was used to assess reliability. The final inter-coder reliability coefficient was 73%, indicating an acceptable level of reliability for the analysis.

### Findings

The findings indicated that the central phenomenon of the study was the application of AI in entrepreneurial skills education, which was influenced by a set of causal, contextual, intervening, strategic, and consequential conditions. The causal conditions included the growing need for new skills, advances in digital technologies, shifts in learning paradigms from traditional models toward interactive learning, and close university–industry collaboration. Among the contextual conditions, five key factors were identified: technological infrastructure, up-to-date educational content, software-related skills, networking and interdisciplinary collaboration, and an innovation-oriented organizational culture. The intervening conditions comprised continuous development of educational content, trend analysis and forecasting of future needs, and automated assessment and feedback.

Four major strategic dimensions were identified: simulation and modeling of real-world environments; project-based learning and problem solving; data analysis and the use of educational dashboards; and intelligent access to learning resources and platforms. According to the perceptions and views of the participants, the application of these strategies was associated with expected or perceived outcomes, including increased educational efficiency, development of key competencies, enhanced innovation, improvement of assessment systems, and greater preparedness for the labor market.

### Conclusion and Contributions

The final model developed in this study demonstrates that AI should be viewed not merely as a technological tool but as a transformative approach to learning management and human capital development. By analyzing students’ learning behaviors and adapting educational pathways to individual needs, AI enables personalized and data-driven learning. Consequently, students can acquire analytical and practical skills while developing greater capacity to generate innovative ideas and establish new ventures.

Furthermore, the use of AI-generated virtual reality scenarios to simulate entrepreneurial environments can provide learners with near-real-world experiences within educational settings, thereby enhancing motivation, cognitive engagement, and creativity. From an educational policy perspective, the findings indicate that the development of technological infrastructure, enhancement of digital literacy and AI competencies among faculty members and students, and design of intelligent educational content are key prerequisites for implementing the proposed model.

In addition, addressing ethical considerations and data security, as well as fostering a culture of acceptance toward emerging technologies among university stakeholders, is essential. The findings also emphasize the importance of sustained university–industry interaction, as such collaboration facilitates the identification of real-world market needs, the transfer of practical experience, and the design of educational programs aligned with evolving economic conditions.

Overall, by employing a grounded theory approach, this study provides a theoretical and practical model for institutionalizing AI in entrepreneurial skills education within medical universities. The findings can serve as a guide for policymakers, educational administrators, and curriculum designers in leveraging the potential of AI to foster the sustainable development of entrepreneurial competencies and to prepare a new generation of intelligent and data-driven entrepreneurs.