JMIR Medical Education

Conversational AI has rapidly become integrated into everyday life, with many individuals using large language model–based systems, such as ChatGPT, Gemini, and Claude, for education, productivity, health information, decision-making, emotional support, and companionship. As patients increasingly incorporate conversational AI into their cognitive, emotional, and social lives, these interactions may influence coping strategies, treatment engagement, clinical decision-making, and mental health outcomes. However, although medical education has increasingly emphasized AI literacy and the responsible use of generative AI by clinicians, comparatively little attention has been given to preparing health care professionals to assess patients’ use of conversational AI during routine clinical encounters. This Viewpoint introduces the AWARE (AI use, why, attachment, reality and risk, and effect on functioning) framework, a practical educational framework designed to help mental health professionals systematically assess patients’ use of conversational AI. Rather than functioning as a diagnostic instrument or psychometric scale, AWARE provides a structured approach to psychiatric interviewing across 5 clinically relevant domains: AI use, why, attachment, reality and risk, and effect on functioning. Together, these domains guide clinicians in exploring patterns of AI use, the motivations underlying engagement, the emotional significance of patient-AI interactions, potential influences on reality testing and clinical risk, and the overall impact of AI on psychological well-being, daily functioning, relationships, and recovery. The framework is intended to complement existing psychiatric interviewing practices by supporting comprehensive history taking, clinical reasoning, risk assessment, documentation, and learner education. We discuss the rationale for routinely asking patients about AI use, review emerging evidence regarding both the potential benefits and risks of conversational AI, and describe how AWARE may be incorporated into undergraduate, postgraduate, and continuing professional education through simulation, objective structured clinical examinations, workplace-based assessment, and clinical supervision. We also outline priorities for future research, including validation, implementation, educational evaluation, and cross-cultural adaptation. As conversational AI becomes increasingly embedded within patients’ daily lives, clinicians require practical approaches to understanding its role in mental health and health care. The AWARE framework offers a structured educational starting point for integrating assessment of patient-AI interactions into routine psychiatric interviewing while supporting patient-centered, evidence-informed clinical practice and health professions education.

Clinical photography can convey phenotypic, anatomical, and diagnostic information that may be difficult to replace with text. This also applies to highly sensitive pediatric clinical photographs when the scope of patient exposure is necessary to achieve a specific educational objective. This viewpoint addresses a more specific problem: “How should the educational function of a highly sensitive pediatric clinical photograph be evaluated when the condition identified with the photograph is substantively described elsewhere in the educational material?” The analysis is grounded in a documented longitudinal observation of 28 editions and reprints of a single academic textbook lineage published between 2005 and 2019, across which the same image-condition configuration persisted. At the point of image presentation, the depicted clinical conditions were explicitly identified, while the accompanying text stated that they were described elsewhere in the textbook, where their substantive clinical descriptions were located in accordance with the textbook’s declared curricular structure. On this basis, I propose educational disconnection to describe a situation in which the image-specific educational contribution at the point of presentation is difficult to reconstruct from the observable relationship among image location, the clinical condition identified with the image, the condition’s declared curricular location, the location of its substantive clinical description, and the destination indicated in the text accompanying the photograph. The central distinction is that identification of a clinical condition, identification of where that condition is substantively described, and identification of the educational contribution of its photograph are not the same. A separate question therefore remains: “What additional educational information does the image contribute here?” The longitudinal dimension extends this analysis through the concept of educational value decay, referring to the possibility that the relationship between an image and the context-specific justification for its presence may weaken over time, without assuming that educational value automatically declines with the age or repeated reproduction of the image. In response, I propose an Educational Reassessment Framework—a practical, event-triggered process that proceeds from identifying the image’s educational function and its relationship to the curricular structure, through assessment of its image-specific educational contribution and exposure-function proportionality, to the consideration of equivalent alternatives, the continuing validity of its justification, and the conditions of responsible continued use. Reassessment is not intended to automatically remove historical, repeatedly used, or highly sensitive photographs. Responsible reuse of an educationally valuable image may remain justified and may reduce the need to photograph additional patients, but educational value does not substitute for appropriate authorization or other conditions of responsible use. Its purpose is to determine whether continued use is supported by an identifiable, current, and context-specific educational contribution, whether the scope of patient information disclosed is necessary and proportionate to that contribution, and whether the conditions supporting use remain appropriate.

Since 2021, the histology of organs in the microscopic anatomy course at the Department of Anatomy II of the Ludwig-Maximilians-Universität Munich has been held as a hybrid course using a 3D virtual microscopy system. Evidence comparing synchronous online versus face-to-face instruction using an advanced 3D virtual slide system remains limited. In particular, it is unclear whether learning outcomes are equivalent across instructional modes, and whether individual student characteristics influence learning outcomes under these conditions.

Accurate interpretation of high-risk acute coronary syndrome (ACS) electrocardiograms (ECGs) is essential for early diagnosis and timely reperfusion, yet substantial deficits persist across health care professions. Digital self-learning formats such as FOAMed (Free Open Access Medical Education) are widely used, but their effectiveness has rarely been evaluated for complex, high-risk ACS ECG patterns. Existing ECG education studies often focus on students or single professional groups and established ST-segment elevation myocardial infarction (STEMI) criteria, leaving newer guideline-recognized STEMI equivalents, selected emerging occlusion myocardial infarction (OMI)–related patterns, and interprofessional emergency care underrepresented.

Hospitals worldwide need to upskill their workforce in advanced AI technologies; yet, published guidance on how to design and deliver such training, particularly in agent-level tools like retrieval-augmented generation (RAG) and the model context protocol (MCP), remains virtually absent.

Cloud-hosted large vision-language models (LVLMs) often outperform open-weight models on multimodal benchmarks, but their applicability to health care examinations that require both text and image reasoning remains unclear. Existing studies on Japan’s National Examination for Clinical Laboratory Technicians mainly used text-only settings and a limited set of models.

Large language models are increasingly proposed as a way to deliver Socratic dialogue at scale in medical education, offering personalized, always-available, and lower-stakes inquiry that human educators cannot logistically provide. In this viewpoint, I argue that this promise is plausible but unproven and that 2 failure modes deserve more attention than they currently receive. The first is the mimicry trap: a system that fluently generates Socratic-sounding questions can appear to cultivate reasoning while functioning as interactive content delivery. I position this as an educational instance of the ELIZA effect and of the proxy-outcome problem; separating conversational mimicry from genuine metacognitive gain remains a central unresolved evaluation problem. The second is what I term the Panopticon Paradox: the data collection that makes AI tutoring effective may erode the psychological safety that Socratic inquiry requires, pushing learners toward performative rather than authentic engagement, in a manner analogous to gaming behaviors documented in intelligent tutoring systems. I present this second construct as a testable causal model with specified mediators, moderators, and falsifiable predictions rather than as an established finding. Because the evidence base is dominated by proof-of-concept tools, cross-sectional surveys, and short-term evaluations, I argue that AI is complementary rather than a replacement technology, and I propose a 3-pillar framework (governance, curriculum, and faculty development) tied explicitly to the 2 failure modes. I distinguish 4 modalities of delegation and argue that ethical deliberation, emotionally complex communication, and ambiguous clinical judgment cannot presently be recommended for autonomous or primary AI delivery.

Digital game–based learning (DGBL) is gaining traction in medical and nursing education, but its integration into public health and health sciences curricula remains limited. This study addresses this research gap by exploring student perspectives, use patterns, and expectations regarding DGBL within German public health study programs.

Technology-enhanced health care interprofessional education (IPE) places high demands on students’ self-regulated learning (SRL) and their ability to work productively with others to prepare them for collaborative practice in health care settings. Yet, little is known about how health professions students perceive and combine their own SRL with coregulation from human and AI-based support in such environments.

Standard setting is essential for a defensible assessment in medical education. The modified Angoff method requires several expert judges, and determining the minimally competent candidate is cognitively challenging. However, empirical evidence on the role of AI in standard setting is unclear.

Indonesia faces 3 interlocking medical workforce crises: an absolute specialist deficit projected to reach 70,000 by 2032 (national density of 0.18 per 1000 population vs the Ministry of National Development Planning [Bappenas] target of 0.28), severe maldistribution (with nearly 59% of specialists concentrated in Java), and a structural anomaly in which residents pay tuition while performing essential clinical work. The 2023 Health Law (Law 17/2023) authorized a transformative reform: a hospital-based residency pathway (Rumah Sakit Pendidikan Penyelenggara Utama [primary teaching hospital; RSPPU]) operating in parallel with the long-established university-based system.

Clinical educators regularly use simulation-based education to help learners develop clinical reasoning, procedural skills, and communication strategies. However, differences between simulated and live clinical environments, such as missing case details or workflow interruptions, can reduce realism and divert learners’ attention from the intended learning goals. While published design guidelines recommend pilot testing simulations, they do not explain how to systematically identify usability problems. Human factors evaluation methods can address this gap by identifying issues before piloting and providing design insights.