Ethical challenges of applying artificial intelligence in teaching and assessing medical students

Abstract

The rapid integration of artificial intelligence technologies into the higher medical education system creates a situation in which traditional models of learning and assessment face new ethical, pedagogical, and institutional risks. In these conditions, the ethical dimension of the digital transformation of education becomes a key factor in ensuring the quality of future doctors' training. The article aims to provide a comprehensive study of the ethical challenges that arise when using artificial intelligence in teaching and assessing medical students, and to determine their impact on the quality of professional training for future doctors. The methodological basis is the methods of critical analysis of scientific sources, structural-logical generalisation, and comparative evaluation, which enabled the systematisation of the spectrum of risks associated with the use of intelligent systems in the educational process. Results. The study identified key ethical issues arising from the implementation of artificial intelligence in the educational process, including algorithmic bias, opacity of decision-making, risks of violating academic integrity, threats to data confidentiality, and changes in the role of the teacher in interaction with students. It is proven that these challenges are cascading, affecting the development of clinical thinking, decision-making autonomy, motivation, and professional responsibility among medical students. It was found that ethical considerations, not the technical characteristics of systems, determine the possibility of safely integrating artificial intelligence into the educational process. The article presents a coherent structural model that outlines the sequence of stages of the application of artificial intelligence in teaching and assessment, proposing approaches to the responsible use of intelligent systems - from risk assessment and transparency of algorithms to ensuring data protection and maintaining academic integrity. The model can be proposed as a practical tool for higher education institutions, helping to reconcile the technological capabilities of artificial intelligence with the ethical standards of medical education. Conclusions. The need for the creation of clearly defined ethical principles and practical mechanisms for regulating the use of artificial intelligence that take into account the specifics of clinical training, patient safety requirements, and the features of future doctors' professional responsibility is substantiated. Priority areas for further research related to the formation of complex models of ethical support for the digital transformation of medical training, the creation of protocols for monitoring the use of intelligent systems and the improvement of quality control mechanisms for assessment with the involvement of artificial intelligence are identified.