JMIR Medical Education

AI is entering clinical practice faster than health professions curricula can teach it, leaving many educators eager to use AI-based teaching tools but unsure of how to build them. Generative AI chatbots—configured as simulated patients, clinical coaches, or formative assessment partners—offer scalable, interactive practice without any programming, yet most educators lack a structured method for designing and deploying them well. This tutorial provides that method: a practical, platform-agnostic workflow for building no-code AI chatbots using widely available large language model platforms. The workflow is organized in five sequential sections that follow the arc of a design project: (1) defining the educational purpose, learner group, and persona; (2) configuring behavior through the system prompt, graduated information disclosure, and structured feedback; (3) refining the learner experience through communication-style calibration, voice interaction, and curated knowledge documents; (4) adding realism and testing, including AI avatar generation and rigorous pilot-testing; and (5) embedding the tool within the curriculum and governing its use ethically. Each section pairs concrete, copy-ready design steps with the reasoning behind them, drawing on the technological pedagogical content knowledge framework and on established learning mechanisms—deliberate practice, self-regulated learning, formative feedback, and simulation-based learning—so that design choices are pedagogically grounded rather than merely technical. Throughout, 2 locally developed initiatives, the Virtual Integrated Patient and the Depression Avatars project, serve as illustrative implementation examples that motivated specific design decisions. These are presented as feasibility and acceptability experiences, not as evidence of educational effectiveness. The tutorial also addresses when a chatbot is not the right tool, the principal risks (hallucination, automation bias, data privacy exposure, and bias in generated personas), and a practical governance checklist for safe deployment. Although the examples are clinical, the workflow is discipline-agnostic and transferable across higher education. No-code AI chatbots are a feasible, accessible way for educators to build interactive learning tools; rigorous, multi-institutional evaluation using validated instruments remains the essential next step.

Virtual reality (VR) offers immersive learning opportunities in health professions education, yet its effects on learner experience and short-term learning outcomes in education on execution of musculoskeletal special tests remain underexplored.

AI is no longer confined to optional decision support; it is becoming a routine presence in clinical workflows, shaping diagnostic hypotheses, triage priorities, risk estimates, and documentation. Yet most educational responses still treat AI as a tool operated by an individual clinician. This framing underestimates how AI reshapes the actual unit of practice: the interprofessional team. We propose the concept of AI-expanded interprofessional collaboration (AI-IPC), in which AI systems function as consequential participants in team cognition—not as moral agents, but as sources of recommendations, uncertainty, and constraints that reorganize communication, authority gradients, and accountability. Building on interprofessional education (IPE) theory, situated learning, and distributed cognition, we argue that “AI literacy” alone is insufficient; learners must be trained to coordinate human-AI-human collaboration in realistic clinical settings. We outline a pragmatic, theory-aligned approach for AI-expanded interprofessional education (AI-IPE): clarifying boundary conditions for AI participation, mapping AI-specific subcompetencies onto established IPE frameworks, and evaluating performance at the level of team behaviors rather than knowledge recall. We present the Interprofessional Education Collaborative (IPEC)–aligned evaluation scaffold with concrete learning activities and assessment approaches, and we outline how the approach can be tailored across undergraduate, postgraduate, and continuing education settings. We further emphasize that implementation requires digital infrastructure, institutional governance, and educator capacity that bridges AI, clinical workflow design, and IPE facilitation. Finally, we address the deliberate use of the “AI colleague” metaphor—not to anthropomorphize AI, but to make the relational and coordinative demands of AI integration visible and teachable. The pedagogical aim is calibrated, critical engagement: teams that verify, question, and, when warranted, override AI contributions rather than defer to them. AI will not replace interprofessional collaboration; it will change what collaboration requires. Education should make that change explicit, rehearsable, and assessable.

Strengthening the global health workforce is central to achieving universal health coverage, but health systems cannot improve what they cannot measure. Valid and scalable assessment of clinical competency is essential for monitoring workforce readiness and ensuring that expanded service coverage translates into high-quality care. Traditional standardized patients, however, remain resource-intensive, difficult to scale, and vulnerable to evaluator-related bias. Recent advances in AI have enabled AI-led simulated standardized patients (SSPs) that may address these limitations.

Emergency nurses must be proficient in operating the Level-1 rapid infusion system to manage hypovolemic shock effectively. However, training opportunities for this infrequently used but life-critical device remain scarce, owing to resource constraints and limited access to equipment. Augmented reality (AR) has emerged as a promising educational technology that provides immersive, hands-on learning experiences without compromising patient safety; yet its application to specialized medical device training in nursing has not been rigorously evaluated.

AI is transforming health care, creating an imperative to integrate AI into medical education. While student perspectives are well-studied, faculty views, particularly in non-Western contexts, remain underexplored.

Generative AI (GenAI) is increasingly integrated into clinical learning and practice. However, medical students often lack the competencies required for safe and critical use, including prompt design, output verification, and recognition of limitations. Educational interventions that integrate GenAI with clinical reasoning frameworks remain limited.

Generative artificial intelligence (GenAI) is increasingly used to draft multiple-choice questions (MCQs) for health professions education, but much evidence concerns raw model outputs, expert ratings, or item difficulty alone. Educators edit GenAI drafts before use, and whether such items are psychometrically ready for postgraduate assessment remains unclear.

Large language models are increasingly used in health professions education; however, the role of prompt design in shaping their outputs remains poorly understood in clinical training contexts. In dentistry, where information presentation, perceived credibility, and procedural reasoning are important, the effects of instructional framing and evidence requirements on AI-generated educational responses are particularly relevant.

AI is increasingly discussed and deployed in health care, yet safe and effective implementation depends on the preparedness, trust, and training of the professionals who are expected to use these tools.

The focus on sustainability in university teaching and education about the climate catastrophe is constantly increasing and is essential for creating change. The chances offered by digital technological innovations are decisive in spotlighting planetary health education. Alongside welfare economies and social movements, they are among the areas with great potential to drive sustainable development.

Large language models in artificial intelligence have been among the tools with a significant and real impact on people’s daily lives. In this regard, they serve as an aid in specific fields, such as education, helping educators with cumbersome tasks such as periodic evaluations.