Generative AI in Scenario-Based Healthcare Education: A Systematic Review of Applications, Validation Practices, and Pedagogical Integration

Overview

Neto and colleagues present a PRISMA 2020 systematic review synthesising 23 empirical studies (from 1,151 initial records across five databases) on Generative AI applications in scenario-, case-, problem-, and simulation-based learning within healthcare education. Using the Mixed Methods Appraisal Tool (MMAT) for quality appraisal and thematic synthesis, they identify six cross-cutting themes centred on a core claim: prompt design in educational contexts functions as a form of instructional specification, encoding the cognitive targets and quality criteria implicit in expert authoring. The review argues that treating prompt design as methodology, standardising multi-stage validation, and formalising Human AI Collaboration are the prerequisites for responsible integration of GenAI into Medical Education and Simulation.

Key Findings

• Only 34.8% of studies aligned generated content with established instructional frameworks such as Bloom's Taxonomy — a gap of design culture rather than technical capability, since specifying cognitive targets within prompts is straightforward yet most implementations treated generation as a text-production rather than an instructional-design problem.
• An equal 34.8% reported prompting strategies in sufficient detail for reproduction, and among those that did, the highest-rated content came from iterative refinement with structured templates — supporting Prompt Engineering and transparent prompt architecture as a methodological norm.
• GPT-4 dominated implementations (44.4%), with OpenAI models underpinning roughly two-thirds of all model instances; Open Source alternatives were under-explored, creating a practical monoculture that compounds reproducibility problems.
• Hybrid human–AI co-creation models outperformed fully automated generation, achieving the highest expert ratings and student satisfaction, with educators positioned as collaborators throughout generation rather than final reviewers only.
• Educational outcomes were generally positive for higher-order cognitive skills (critical thinking, clinical reasoning) but inconsistent for factual knowledge acquisition, suggesting GenAI-assisted learning preferentially supports reasoning over recall.
• Scalability was the primary adoption driver yet efficiency gains were largely unquantified — only one study measured authoring time (a ~33% reduction) — while documented quality failures (16.67% hallucination rate in ophthalmology cases, 50% accuracy agreement in pharmacy PBL) persisted across the review period, indicating a structural rather than temporary problem.

Implications for Practice

• Treat prompt design as a methodological element on par with instrument selection, disclosing system prompts, refinement steps, and evaluation criteria so that instructional design is not rendered invisible by unreported prompting.
• Prioritise pedagogical alignment by specifying cognitive targets aligned with established taxonomies and structuring learning progressions explicitly before deployment, rather than treating alignment as an optional enhancement.
• Standardise multi-stage validation — the proposed four-stage pipeline (automated checks, clinical expert review, pedagogical alignment, and learner pilot testing) addresses failure modes that single-point validation cannot catch.
• Formalise Human AI Collaboration and define educator roles across the workflow, since hybrid co-creation consistently outperforms both full automation and minimally guided use.

Connected Concepts

• Generative AI
• Medical Education
• Problem Based Learning
• Simulation
• Meta Analysis Systematic Review
• Human AI Collaboration
• Prompt Engineering

Connected Articles

• GenAI Simulate Patient History Pbl 2026 — GenAI simulating patient history within problem-based learning
• Pbl Biomedical Engineering GenAI 2026 — problem-based learning with GenAI in biomedical engineering education
• Hdr Brachytherapy Agentic AI Simulation 2026 — agentic AI simulation in healthcare training
• AI Teammate Task Distribution Medical Training 2026 — AI teammate task distribution in medical training (human–AI collaboration)

Citation

Generative AI in scenario-based healthcare education: A systematic review of applications, validation practices, and pedagogical integration — Neto, M., Pinto, R., Reis, J., & Antão, L. (2026). Computers and Education: Artificial Intelligence, 11, 100654.