en Medical Education Medical Education Debate Article Debate Article <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">Understanding concepts through the accommodation discourse is vitally important for linguistic agreements. In WFME standards, concepts are regarded as milestones of quality assurance and improvement; accordingly, the study was designed in three phases. In the first phase, a nationwide expert panel utilized structured focus group discussions as the primary consensus mechanism to extract and rank significant concepts. In the second phase, conducted during a dedicated workshop at the WFME International Conference in Thailand, a formal survey was administered to 50 international participants to rank the concepts and gather qualitative justifications. In the third phase&mdash;which is the focus of this paper&mdash;results from the two previous rounds were employed using Generative Text-driven AI (Perplexity) to formulate cogent arguments based on the principles of reasoning. Utilizing an expertise-oriented evaluation approach, three structured arguments were designed based on the authors&rsquo; experiences. The dialogue examined the relative importance of key WFME concepts, the primacy of governance as the mechanism underpinning mission and values, and the intertwinement of governance and social accountability through a &ldquo;domino&rdquo; metaphor. The AI&rsquo;s responses demonstrated that argumentation-based prompting can guide coherent reasoning without direct citation support, highlighting governance as a foundational construct influencing mission alignment, accountability mechanisms, and contextual adaptation. Furthermore, the analysis contrasted top-down governance models with bottom-up approaches more applicable to developing contexts. Findings suggest that text-driven AI can function as a reflective partner in standards interpretation, provided that arguments are linguistically precise and grounded in expertise.</span></span></span></span></span></span></span></span><br> &nbsp; Medical Education, WFME Standards, Governance, Social Accountability, Artificial Intelligence, Program Evaluation 931 934 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-10162-1&slc_lang=en&sid=1 Mahdi Aghabagheri mahdi.aghabagheri@gmail.com 2003194753284600100581 2003194753284600100581 No Medical Education Department, Medicine School, Shahid Sadoughi University of Medical Sciences, Yazd, Iran Abdoljavad Khajavi Abjkhajavi@yahoo.com 2003194753284600100582 2003194753284600100582 No Department of Community Medicine, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran Ali Heyrani ali.heyrani@hums.ac.ir 2003194753284600100583 2003194753284600100583 No Social Determinants in Health Promotion Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas Iran Masoud Khandehroo Khandehroo.m@gmu.ac.ir 2003194753284600100584 2003194753284600100584 Yes Department of Management Sciences and Health Economics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Abtin Heidarzadeh heidarzadeh@gums.ac.ir 2003194753284600100585 2003194753284600100585 No Medical Education Research Center, Education Development Center, Guilan University of Medical Sciences, Rasht, Iran