This study presents a systematic literature review to explore the emerging role of Generative Artificial Intelligence (GAI) in entrepreneurship education (EE). While recent years have witnessed an increasing interest in applying GAI tools, such as ChatGPT, to support personalized learning, business model simulation, and decision-making, the field remains at an early exploratory stage. Drawing on 50 peer-reviewed journal articles published between 2019 and early 2025, the study aims to map the conceptual landscape, identify key themes, and assess the current state of theoretical and empirical development. The findings reveal four major domains in GAI-enabled EE: personalized and adaptive instruction, simulation-based entrepreneurial training, ethical and psychological concerns, and ecosystem integration through intelligent systems. Although promising use cases are identified, most studies are either conceptual or limited to small-scale exploratory evidence. As such, the long-term impact of GAI on entrepreneurial competence, mindset, and outcomes remains uncertain and calls for empirical caution. To bridge this gap, this study proposes a set of research propositions and a conceptual framework that integrates micro-level instructional applications with meso-level competency development and macro-level ecosystem readiness. These are grounded in educational theories, including constructivism, experiential learning, and social cognitive theory. In addition, the review suggests methodological strategies, such as structural equation modeling (SEM), hierarchical linear modeling (HLM), and longitudinal mixed-methods designs, to test GAI's influence across cultural and institutional contexts rigorously. Furthermore, the study discusses actionable implications for educators, policymakers, and researchers, emphasizing the importance of ethical guidance, digital equity, and cross-contextual adaptability. It advocates for designing GAI-integrated curricula that foster not only technical proficiency but also critical thinking, creativity, and ethical reasoning. By systematically synthesizing current findings while avoiding overgeneralized claims, this review contributes to clarifying the conceptual boundaries of GAI in EE and sets the foundation for future empirical exploration.