This study aims to analyze how AI platforms are influencing entrepreneurship education, focusing on both technological advancements and pedagogical changes introduced by their integration. Specifically, it examines how Large Language Models (LLMs), Predictive Analytics, and Natural Language Processing (NLP) contribute to reshaping the delivery and design of entrepreneurship curricula. These technologies enable automated content creation, predictive insights into entrepreneurial trends, and dynamic conversational support, which increasingly influence how students identify opportunities, structure business plans, and test entrepreneurial ideas within educational settings. From a pedagogical perspective, the study focuses on how AI platforms are transforming case-based learning, personalized learning, and ethical awareness development, which have long been core pillars of entrepreneurship education. AI platforms offer real-time adaptive learning environments, where students receive customized feedback based on their evolving entrepreneurial projects. However, they also raise concerns about over-reliance on algorithmic outputs, potentially reducing student autonomy and critical reasoning when developing entrepreneurial strategies. This study adopts a conceptual approach, conducting a theoretical analysis grounded in existing university-level case studies and academic literature on AI-supported entrepreneurship education. The study provides academic implications by highlighting the need to balance AI’s efficiency with pedagogical depth, while offering policy implications for institutions and regulators to develop ethical guidelines and governance frameworks that ensure AI adoption supports, rather than undermines, entrepreneurial learning processes and ethical decision-making.