Research in Health and Medical Sciences

en Artificial Intelligence in Clinical Education: Strategies, Methods, and Techniques: a Commentary Study Artificial Intelligence in Clinical Education: Balancing Humanism and Technology in Ethical Frameworks – A Commentary تخصصي Special Medical education stands at the intersection of tradition and innovation. The explosion of the growth of medical knowledge, the increasing sophistication of health systems, and the advent of a new generation of digital learners who demand instant access, tailored content, and interactive engagement have imposed an unprecedented level of pressure on traditional educational systems [1]. The inherent limitations of patient-based education, including inequity in learning opportunities, ethical concerns, and issues of patient safety, have also highlighted the need for a paradigmatic shift in clinical education pedagogy [2]. To address this paradigmatic shift, this discussion is conducted based on theoretical models of technology-enriched instructional design. In this context, the SAMR model is used as a framework for assessing the level of technology integration in the learning process. The model has four levels: substitution, augmentation, modification, and redefinition, each of which demonstrates the transformative potential of technology in the learning environment [5]. Additionally, the TPCK model, based on the synthesis of three facets: technology knowledge, pedagogy knowledge, and content knowledge, is employed to study the successful integration of these factors in clinical education [6]. The model enables the identification of the most effective combination for achieving effective and sustainable learning. The collaborative use of these two models provides a theoretical and practical platform to gain insights into how AI can be integrated into medical education, as well as the groundwork on which cutting-edge and evidence-based educational programs can be built. Artificial intelligence and its spawn, machine learning (ML) and natural language processing (NLP), have been born not as replacement teachers but as cognitive augmentators and facilitators. AI is able to review vast amounts of educational and clinical data to learn patterns of education, identify gaps in knowledge, and adapt education interventions in real time [3, 7, 8]. This ability is in the vanguard of the movement toward competency-based medical education (CBME), where progression is gauged by the attainment of competence [4].   Artificial Intelligence, Clinical Education, Strategies, Methods, Techniques, Active Learning 29 32 http://jrhms.thums.ac.ir/browse.php?a_code=A-10-101-4&slc_lang=en&sid=1 Mohsen Keshavarz keshavarz_mohsen@yahoo.com 10031947532846001318 10031947532846001318 No Department of E-Learning in Medical Sciences, School of Paramedical Sciences, Torbat Heydariyeh University of Medical Sciences, Torbat 1 Heydariyeh, Iran Leili Mosalanejad 10031947532846001319 10031947532846001319 Yes Research Center for social determinants of health, Department of Medical Education, Jahrom University of Medical Sciences, Jahrom, Iran Fateme Rahmanian فاطمه رحمانیان 10031947532846001320 10031947532846001320 No Internal Medicine, Jahrom University of Medical Sciences, Jahrom, Iran