The emergence of large language models (LLMs) is substantially transforming health education by introducing novel teaching methods and educational practices. Their potential is considerable and extends across multiple levels, including curriculum design, resource generation, personalized learning support, assessment support, and assistance with academic writing and bibliographic management [1-7].

In health professions education (HPE), one key challenge relates to the development of case-based educational materials, which are widely used to promote clinical reasoning, knowledge integration, and decision-making in authentic contexts [8]. For the purpose of this review, case-based materials were defined as text-based educational resources that present realistic clinical situations requiring learners to apply knowledge, interpret information, and make decisions [9]. These materials include clinical vignettes or cases [10], which summarize patient situations to support reasoning and discussion; case-based questions [11], which combine clinical cases with assessment items, such as multiple-choice questions (MCQs), and are commonly used in high-stakes examinations; and simulation scenarios [12], which consist of structured scripts designed for use in simulated environments. Evidence indicates that case-based approaches lead to greater improvements in learner knowledge and performance compared with traditional lectures [13-16]. However, the development of high-quality case-based materials is resource-intensive: cases must be clinically coherent, aligned with learning outcomes, appropriately challenging, and frequently refined through expert review and psychometric testing [17-19]. These requirements can limit the size, diversity, and timely updating of case libraries and assessment banks, particularly where faculty time or specialty expertise is limited.

LLMs offer novel opportunities for the generation of case-based educational materials. Recent reviews have examined the broader use of LLMs in HPE [1-3]. Lin et al [1] synthesized applications across medical education, including educational resources, teaching methods, and assessment, and highlighted both opportunities and implementation challenges. Basil et al [3] mapped the broad integration of generative AI (GenAI) across HPE, identifying applications in curriculum design, content creation and delivery, personalized learning, assessment, automated grading, and feedback. Similarly, Astbury et al [2] reviewed the use of GenAI for teaching and assessment across health professions and emphasized its growing role in learning resource development and educational assessment. These reviews demonstrate the expanding role of LLMs in HPE but address case-based generation only as 1 component of a broader educational landscape. Notably, Basil et al [3] identified content creation as the predominant area of GenAI integration in HPE, highlighting the growing importance of this application. Given the central role of case-based materials in HPE, a more focused and detailed synthesis of their generation using LLMs is therefore warranted.

A more focused scoping review by Kaya et al [20] examined LLM-generated clinical cases and vignettes. However, its scope was limited to cases and vignettes intended to function as standalone cases and explicitly excluded case-based MCQs and other formats combining clinical scenarios with assessment activities. In addition, although prompting and evaluation approaches were reported, their methodological characterization remained relatively broad, focusing mainly on prompt reporting and single versus iterative prompting, while evaluation findings were synthesized primarily in terms of overall case quality and bias. A more comprehensive and granular synthesis encompassing a broader spectrum of case-based educational materials and providing greater detail on their generation and evaluation processes therefore remains needed. The current review addresses this gap by encompassing clinical cases and vignettes, case-based questions, and simulation scenarios, and by providing a detailed characterization of the models and reference materials used, prompting and refinement approaches, evaluation methods and criteria, and reported findings and challenges across multiple quality domains.

The objective of this scoping review was to synthesize the current evidence on the use of LLMs to generate case-based materials in HPE. Specifically, the review sought to examine how these models were used to generate case-based educational materials, how the resulting materials were evaluated, and what findings and challenges were identified. To achieve this objective, the review addressed the following questions: (1) How are LLMs being used to generate case-based materials in HPE? (2) How are these LLM-generated case-based materials evaluated? (3) What key findings and challenges are reported in the existing literature?

This review was guided by the Joanna Briggs Institute methodology for scoping reviews and reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines [21]. The completed PRISMA-ScR checklist is available in Checklist 1.

A comprehensive literature search was conducted in 7 electronic bibliographic databases: PubMed, Embase, Scopus, Web of Science, CINAHL, ERIC, and IEEE Xplore. Searches were conducted on May 10, 2026, and covered studies published from January 1, 2017, onwards. All retrieved records were imported into Rayyan (Rayyan Systems Inc), a web-based systematic review management tool, for screening and study selection [22]. Duplicate records were identified using Rayyan’s automated duplicate detection function and subsequently verified through manual review by 1 author (ZZ).

The search strategy was initially developed by reviewers EL and ZZ and subsequently refined by the review team. It combined controlled vocabulary terms adapted to the database (eg, MeSH and Emtree terms) with free-text keywords, related to 3 concepts: (1) GenAI and LLMs, (2) case-based educational materials, and (3) HPE. The search strategy was iteratively refined in PubMed, and search terms and syntax were subsequently adapted to the indexing and search functionalities of each database. An initial search was conducted on September 8, 2025, and updated on May 10, 2026, following expansion of the search strategy and database coverage. Corresponding authors were contacted by authors (ZZ and ST) when full-text articles could not be obtained through institutional access. The complete database-specific search strategies are provided in Multimedia Appendix 1. Reporting of the search strategy followed the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Literature Search Extension) guidelines [23] (Checklist 2).

Study selection was conducted using Rayyan in 2 stages: (1) title and abstract screening and (2) full-text review. At each stage, records were assessed independently by 2 reviewers (ZZ, AS, or ST) against the predefined eligibility criteria. Discrepancies were resolved through discussion and consensus. A third reviewer (RT) was consulted in 2 cases where consensus could not be reached.

A structured data charting form was developed in Microsoft Excel to extract relevant information from the included studies. The form was piloted through independent data extraction by 2 reviewers (ZZ and AS) on 3 randomly selected studies and subsequently refined to improve clarity, consistency, and completeness [24]. Data charting was then performed by 1 reviewer (ZZ, AS, or ST) and independently verified by a second reviewer (ZZ, AS, or ST) [24]. Any discrepancies were resolved through discussion and consensus.

Data charted from the included studies [25-96] comprised bibliographic characteristics (authors, title, year, journal, and country); educational context (health profession and learner level); study characteristics (study type and objectives); characteristics of the LLM application (task performed by the LLM, model used, input data or reference materials, prompting strategy, prompt availability, and human oversight); characteristics of the generated case-based materials (type of material and sample size); evaluation approaches (evaluation methods and criteria); and study outcomes, including key findings and reported challenges or limitations. The complete data charting form is provided in Multimedia Appendix 2.

Charted data were synthesized using descriptive statistics and narrative synthesis. Quantitative data were summarized using frequencies and percentages where appropriate. Findings were organized to address the review objectives and research questions, including (1) characteristics of the included studies [25-96], (2) technical and methodological approaches to LLM-based case generation, (3) evaluation methods and criteria used to assess generated materials, and (4) key findings and reported challenges. Results are presented using tables, narrative summaries, and visual evidence-mapping approaches, including a geographic distribution map and an evidence gap map, to facilitate comparison across studies and identify patterns and gaps in the literature.

A total of 2777 records were identified through 7 databases and citation tracking. After removing 1497 duplicate records, 1280 unique records were screened by title and abstract, with 120 retained for full-text review. Of these, 110 were successfully retrieved and assessed for eligibility, leading to the exclusion of 39 full-text articles. Reasons for exclusion included the absence of LLM-based case generation (n=27), insufficient methodological detail (n=4), non-English publications (n=4), lack of LLM use (n=1), commentary articles (n=1), study protocols (n=1), and framework development papers (n=1). One additional study was identified through citation searching and met the inclusion criteria. Ultimately, a total of 72 studies [25-96] were included in the final synthesis (Figure 1). Detailed charted information for all included studies is provided in Multimedia Appendix 2.

Most studies were published in 2024‐2025 (53/72, 74%), although an additional 14 studies [25-38] had already been published in 2026 by the beginning of May, highlighting the continued expansion of this field. The included studies most frequently originated from the United States (19/72, 26%) [27,35,44,48,53-55,57,58,60,62,63,74,81,83,87,88,90,95] and Türkiye (10/72, 14%) [28,38,42,47,49,65,72,73,76,93] (Table 1). Geographically, the evidence was concentrated in Asia (35/72, 49%) [26,28,30,31,33,36,38,41,42,45-47,49,50,52,56,65,67-73,76,80,82,84-86,89,91,93,94], and North America (27/72, 37%) [27,35,37,40,44,48,51,53-55,57,58,60-63,66,74,75,78,81,83,87,88,90,95,96], with fewer studies originating from Europe (10/72, 14%) [25,29,31,34,39,43,64,77,79,92], Africa (3/72, 4%) [31,32,43], and South America (1/72, 1%) [59], while no studies were identified from Oceania (Figure 2).

The most frequent study design was LLM evaluation studies without comparison groups (30/72, 42%) [25,26,31,33,35,36,40,43,44,46,51,53-55,57,58,61,62,68,69,71,74,76,79,81,83,86,87,89,90]. Comparative approaches were also common and included comparisons between LLM-generated and expert-generated materials (15/72, 21%) [30,37,41,45,48,49,52,63,64,66,67,72,75,77,96], between different LLMs (13/72, 18%) [27-29,38,39,42,50,56,60,70,80,85,91], or between LLMs or other generation methods and expert-generated materials (4/72, 5%) [32,47,65,73]. Descriptive or exploratory studies accounted for 10/72 (14%) [34,59,78,82,84,88,92-95] of the included publications (Table 1).

Case-based MCQs were the most frequently generated materials (28/72, 39%) [29,36,39,42,44,45,47-49,55,56,58,64-66,69,70,73,74,76,79-85,93], followed by clinical cases or vignettes (22/72, 30%) [26,30,31,37,40,41,50,53,57,59,67,68,70-72,78,86,89-92,94] and simulation scenarios (18/72, 25%) [27,28,32-35,46,51,52,54,60,62,63,77,87,88,95,96]. Medical education was the predominant focus (59/72, 82%) [25,28-31,34,36-45,47-54,56-58,61,63-66,69-86,88-96], and undergraduate students were the most commonly targeted population (42/72, 58%) [25,26,28-30,32,36-42,45,47,49,51,53-56,58,60-62,65,67,69,70,72,73,75-79,81,83-85,87,91,95], although student level was not specified in nearly one-third of studies (Table 1). The evidence gap map (Figure 3) highlighted substantial variation across health professions. Medical education studies covered all material types, particularly case-based questions, whereas nursing studies (7/72, 10%) [33,35,46,60,62,67,87] were concentrated on simulation scenarios. Pharmacy studies (2/72, 3%) [32,55] generated case-based questions and simulation scenarios, while studies involving other health professions (5/72, 7%) [26,27,59,68,96] predominantly generated clinical cases or vignettes. Few studies targeted postgraduate or continuing education learners, revealing important gaps in the current evidence base.

This scoping review showed that the use of LLMs to generate case-based materials in HPE has expanded rapidly since 2023, predominantly within undergraduate medical education and largely focused on the production of case-based MCQs and clinical vignettes. In the 72 studies [25-96] reviewed, LLMs were mostly used with simple prompts. More advanced strategies, such as few-shot prompting, prompt chaining, or using external knowledge sources, were rarely applied. Evaluation was usually based on small expert reviews, while psychometric testing and student-based validation were less common. Overall, LLM-generated materials were frequently judged to be clinically plausible, structurally clear, and reasonably aligned with educational objectives, particularly after refinement. However, consistent limitations emerged, including factual inaccuracies, construction flaws, superficial clinical realism, pedagogical misalignment with curricula or training level, suboptimal psychometric performance, and recurrent demographic and epidemiological bias. Finally, comparative studies reported varying performance across LLMs, with no model consistently outperforming others. However, methodological differences between studies limit conclusions about which models perform best.

Our findings are consistent with previous reviews examining the use of GenAI in HPE. Basil et al [3], in a broad scoping review of 241 studies, identified content creation as the predominant educational application of GenAI, followed by assessment, with medicine and nursing accounting for most published research. They also highlighted generally positive perceptions of GenAI alongside ongoing concerns regarding accuracy, ethical considerations, governance, and the need for more rigorous evaluation of educational outcomes. Similarly, Astbury et al [2] reviewed 23 studies focused on teaching and assessment and found that GenAI was primarily used for learning resource development, assessment, feedback, and simulation, with reported benefits including personalized learning, improved efficiency, and resource use, but persistent concerns regarding accuracy, inconsistent outputs, algorithmic bias, and academic integrity. Our findings reinforce these broader observations while providing a focused and comprehensive synthesis of one important educational application: the generation of case-based educational materials.

A recent review, Kaya et al [20], specifically examined LLM-generated cases. Our findings broadly aligned with their conclusion that LLM-generated cases represent a promising but evolving educational resource, supported by a heterogeneous evidence base. Similar to Kaya et al [20], we found that GPT-based models were the most frequently used LLMs, medicine was the predominant health profession represented, and expert evaluation remained the primary method for assessing generated materials, underscoring the continued need for human oversight before educational implementation. Consistent with their findings, we also observed that prompts were reported in most studies, although prompting approaches varied considerably. However, whereas they primarily described whether the prompt was reported and the use of single versus iterative prompting, our review extends these findings by providing a more granular characterization of generation approaches. In addition to reporting prompt use, we examined the use of reference materials, prompting techniques, and refinement processes, showing that generation predominantly relied on relatively simple prompting strategies with limited adoption of more advanced prompt engineering. Furthermore, while the authors described quality evaluation in broad categories, such as overall case quality and bias, our review also offers a more detailed synthesis of quality assessment by integrating both evaluation methods and findings across multiple quality domains, including clinical accuracy, structural quality, educational alignment, pedagogical quality, realism, psychometric performance, learner perceptions, and bias. This broader perspective provides a more comprehensive understanding of current practices, methodological limitations, and evidence gaps. Finally, our review substantially captures a wider spectrum by synthesizing more than 3 times as many studies, encompassing a broader range of case-based educational materials, including clinical cases, case-based MCQs, Objective Structured Clinical Examination (OSCE) or Objective Structured Practical Examination (OSPE) scenarios, simulation scenarios, script concordance tests (SCTs), and short-answer questions.

This review has limitations related to its scope and methods. First, although a comprehensive search strategy was conducted across 7 major databases, some studies may have been missed due to language limitations (ie, English) and exclusion of gray literature and other databases (eg, Cochrane Library). Moreover, given the rapid development of LLMs in education, recent studies may not have been included; therefore, these findings should be interpreted as a picture of the current evidence. Second, consistent with the exploratory approach of scoping reviews, the methodological quality (including study bias) of the studies was not formally assessed, which could limit conclusions regarding the strength of the evidence. Additionally, the included studies [25-96] were heterogeneous regarding their design, educational settings, generation approaches, and outcome measures, limiting direct comparisons and conclusions about which approaches may improve the quality, reliability, or educational usefulness of LLM-generated materials. Future systematic reviews and meta-analyses could investigate the comparative effectiveness of different generation and refinement approaches. Finally, the review focused only on text-based case materials to maintain clarity and comparability. Studies involving multimodal case generation, such as images or videos, were outside the scope of this review.

This scoping review provides a comprehensive synthesis of the rapidly evolving literature on the use of LLMs to generate case-based materials in HPE. Unlike previous reviews that examined GenAI across multiple educational apps or focused primarily on clinical cases, this review integrates evidence across a broad range of case-based materials while also synthesizing generation approaches, prompting strategies, evaluation methods, reported findings, and challenges. The mapped evidence suggests that LLMs have the potential to support the development of case-based materials, but current approaches continue to require expert oversight because of recurring concerns regarding clinical accuracy, educational quality, psychometric performance, clinical realism, and bias. The review also identifies important evidence gaps, including the predominance of studies in undergraduate medical education, limited evidence across other health professions, the reliance on small expert-based evaluations, limited adoption of advanced generation approaches, and the absence of standardized evaluation frameworks. By synthesizing the current evidence and identifying methodological and evidence gaps, this review provides educators, curriculum developers, and researchers with a structured overview of this rapidly evolving field and highlights priorities for future research. As evidence and LLMs continue to evolve, the findings of this review may inform the development of more robust generation workflows, more consistent evaluation approaches, and the responsible use of LLM-generated case-based materials in HPE.