Impact and Future Outlook of Artificial Intelligence in Universities and Medical Education

Given the substantial influence of emerging artificial intelligence (AI) technologies on contemporary educational methodologies, a narrative literature review was performed. In higher education (HE), AI has influenced teaching methods, research, innovation, and administration. Examples include tutorial systems, adaptive learning environments, assessment tools, and systems for identifying educational gaps, among others. The implementation of AI across diverse educational contexts has demonstrated substantial effects on a wide range of activities involving students, faculty, researchers, professional and administrative staff. Undoubtedly, these technologies raise various ethical challenges and dilemmas related to academic integrity that must be addressed. Medical education (ME) faces multiple challenges, including an increasingly diverse and technologically connected student population, faculty availability, and restricted access to clinical settings. In this context, AI emerges as a transformative tool that reshapes teaching and learning methods, assessment, and clinical learning. Its capacity to analyze large data sets, recognize patterns, and support automated decisions opens new possibilities for personalizing the education of medical and other health professionals. In recent years, ME has undergone numerous transformations and the introduction of various technological innovations, which have had an impact on new teaching methods and student training. In short, AI offers a great opportunity to strengthen the quality of higher and medical education through initiatives that promote interactivity and a student-centered approach. New forms of teaching are being proposed through roles such as facilitator, mentor, and teaching guide. Contemporary teacher training must incorporate enhanced digital skills and a critical attitude toward emerging technologies from an ethical standpoint.