Empowering Educators to Build AI Chatbots in Health Professions Education: Tutorial on a No-Code Design Workflow.

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.