Entrepreneurship education (EE) aims to deliver various entrepreneurial outcomes. One such entrepreneurial outcome (EO) is entrepreneurial intent (EI), which has been touted as one of the most significant determinants of a new business. Entrepreneurial mentoring (EM) has been linked and indirectly measured for its relationship with improved EO and EI. In countries in the Global South, such as the Philippines, there are only a few qualified instructors and mentors for entrepreneurship. Due to the lack of qualified faculty to do mentoring in universities, this study aims to turn to computer-based alternatives to address the under-resourcing of qualified human faculty. This study introduces an approach using Generative AI (GAI) and Large Language Models (LLMs) to simulate startup modeling mentoring to enhance EE by bridging these gaps. The coaching experience is intended to be contextual and adaptive based on the student’s input and interactions. An area of mentorship is financial modeling as a replacement for traditional financial forecasting methods. Using the Business Model Canvas (BMC) framework, the proposed simulator employs natural language processing (NLP) to offer real-time feedback and support to student startups. This research evaluates the impact of these simulations on learning efficacy, satisfaction, and entrepreneurial intent. It proposes a pre and post-test quasi-experimental design during implementation with live participants iteratively across several school semesters. However, before the sessions with humans, the study can already make preliminary tests using GAI agents as simulated personas during development. Once implemented, the findings may provide insight into the potential of simulated mentoring as a scalable and effective educational tool in entrepreneurship, offering significant insights into integrating AI technologies in enhancing entrepreneurial learning environments.