SCALES-AI: A Supervision- and Context-Aligned Entrustment Framework for Integrating Artificial Intelligence into Emergency Medicine Education

Abstract

The rapid entry of artificial intelligence (AI) into emergency medicine (EM) education demands a practical way to decide how much autonomy to grant specific tools for specific educational tasks. We propose SCALES-AI (Supervision- and Context-Aligned Levels of Entrustment for AI in Education), a theory-informed rubric that rates AI tools—rather than trainees or faculty—on an entrustment scale for educational (not clinical) use. Developed through a targeted literature synthesis, a three-workshop multidisciplinary process, and stakeholder refinement, SCALES-AI links four Foundational Principles (human-centered augmentation, evidence-based scalability, context-specific adaptation, and an ethical foundation) to four Dimensions of Trustworthiness (Ability, Integrity, Benevolence, and Equity). These dimensions inform a five-level entrustment scale (0–5), ranging from “not appropriate” to “full autonomy for low-stakes tasks,” with supervision intensity and audit cadence matched to level and re-evaluation triggered by model, prompt, corpus, or guideline changes and by observed drift. We operationalize each dimension with example indicators and illustrate use through concrete scenarios (e.g., documentation feedback, practice-question generation, simulation debrief support, portfolio analytics) mapped to appropriate levels. To support implementation and reproducibility, we provide a SCALES-AI Checklist to document tool-task ratings, supervision and scope controls, and monitoring plans, and a SCALES-AI Inter-Rater Calibration Template to record independent ratings and consensus, enabling local reliability assessment. Although motivated by the high-stakes, time-pressured EM environment, SCALES-AI is generalizable across health professions education. Treating AI-tool trustworthiness as dynamic and context-dependent enables safe, equitable, and auditable adoption while preserving essential human elements of medical training.

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