Emeh Blessing
The accelerated adoption of artificial intelligence in medical education has produced both great opportunities in terms of improving teaching, learning, and evaluation and also the crux of the matter of academic integrity, ethical governance, and educational quality. This systematic review has attempted to compile existing program of artificial intelligence, generative artificial intelligence, large language models, adaptive learning, intelligent tutoring systems and simulation-based learning in medical education, particularly in terms of teaching practices, instructional outcomes, and academic dishonesty concerns. Within the framework of PRISMA, the search of pertinent literature was conducted. Research about AI-assisted teaching, personalized learning, virtual patients, automated feedback, assessment analytics, clinical reasoning, plagiarism, academic misconduct, and ethical AI were located. The results show that AI has revolutionized medical curriculum delivery in the factors of personalization in the learning pathways, predictive analytics, natural language processing, competency-based education, and simulated environment. Large language models and ChatGPT have shown potential to improve student engagement, clinical decision support, formative assessment, and learner autonomy. Yet, significant issues and fears of algorithmic bias, data privacy, explainable AI, professionalism on the internet, offloading human cognition, overweighting on autopilot processes remain. Among the most commonly reported risks were the academic integrity issues, such as plagiarism and cheating detection, fake citations and unauthorized AI-assisted assessment. The review has shown that effective implementation of artificial intelligence in healthcare education needs to be accompanied by effective ethical governance, faculty training, AI literacy training, clear regulatory frameworks, and human-AI partnership.