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The use of artificial intelligence (AI) in higher education entrepreneurship education (EE) is an important advancement that integrates innovative technology with teaching methods.  Even so, the research output in this new field is still disjointed and unclear. This study delineates key trends, prominent authors, thematic evolution, and collaboration patterns using a bibliometric analysis of 50 publications from the Web of Science database (2016-2025). Drawing on the output from Biblioshiny software, the results indicate a rapid growth in research output after 2022. This is largely attributed to generative AI developments.  Key areas of research have, also, shifted over time from a general focus on pedagogy to groupings that focus on technology, such as using AI in education, focusing on student consequences, and entrepreneurial intentions. The results show a leading contribution to research output from China. International partnerships, on the other hand, are decentralised. Also, the overall network of co-authors is still fragmented. The study’s conclusion indicates that the field is transforming into a synthesis of technological and human-centric frameworks showing an interdisciplinary convergence of education, psychology, and computer science. It also points out more research avenues, such as the need for more qualitative studies on implementation and a closer look at the ethical side of AI in education.

This study presents a systematic literature review analysing the impact of artificial intelligence (AI) on higher education, focusing on its methods, results, and implications. By synthesising a diverse range of academic papers, the review explores how AI technologies influence educational standards and practices in higher education institutions. Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies. These capabilities position AI as a transformative tool for improving educational delivery and outcomes. However, the study also highlights significant challenges associated with integrating AI into higher education. These challenges include delineating the appropriate scope of AI use, addressing inequalities in access to digital resources, and ensuring adequate training and support for educators and students. The review underscores the importance of understanding these complexities to guide the development of effective strategies and policies that optimise AI's potential while mitigating its limitations. The review offers critical insights into the dual role of AI in higher education, where it can either advance or hinder educational standards depending on how it is implemented. By examining the advantages, limitations, and broader consequences of AI-powered instructional tools, this study provides a comprehensive perspective on the intricate relationship between AI and educational quality. The findings aim to inform educators, policymakers, and stakeholders about the opportunities and challenges of adopting AI in higher education, contributing to the development of inclusive, innovative, and sustainable educational practices.