What They Studied

Abd-Alrazaq and colleagues reviewed the emerging role of large language models in medical education, examining how tools like ChatGPT might support student learning through case-based exercises, feedback generation, study support, and clinical reasoning practice. They explored both the opportunities these technologies present and the challenges that must be addressed before widespread educational adoption.

What They Found

• LLMs showed genuine utility for generating realistic clinical case scenarios that students could use for practice and self-assessment.
• The models could provide immediate, detailed feedback on student responses, offering explanations that help learners understand not just what is correct but why.
• Students reported that LLM-based study tools were helpful for consolidating knowledge and preparing for examinations.
• Significant concerns emerged around accuracy: models sometimes provided plausible but incorrect clinical information that students might not have the expertise to identify.
• The authors identified a risk of over-reliance, where students might substitute AI interaction for the mentored clinical reasoning that develops through human supervision.

Methodology

This was a review and analysis of early evidence on LLM use in medical education, synthesizing published studies, pilot programs, and expert commentary. The authors applied an educational technology framework to evaluate opportunities and challenges. As an early-stage review, much of the evidence base consisted of small pilots and descriptive studies rather than controlled trials.

What This Means for SLPs

• This is directly relevant to Clinical Fellowship Year (CFY) supervision and graduate student clinical education in speech-language pathology.
• LLMs could supplement supervision by generating practice cases in areas where a clinical site has limited caseload diversity (for example, creating fluency or voice disorder scenarios at a pediatric language site).
• AI-generated feedback on student clinical writing (SOAP notes, evaluation reports) could give students additional practice opportunities between supervisor review sessions.
• The accuracy concern is critical for SLP education: a student practicing with an AI tool that provides incorrect information about, say, swallowing physiology or phonological development could develop fundamental misconceptions.
• The ideal model is AI as a supplement to supervision, not a replacement, giving students more repetitions while the supervisor provides the clinical judgment and mentorship that AI cannot.

Limitations to Keep in Mind

• The evidence base was drawn entirely from medical education; no studies have specifically evaluated LLM use in speech-language pathology or communication sciences education programs.
• Most of the reviewed evidence was preliminary, involving small samples and limited outcome measurement.
• The rapid pace of AI development means both the capabilities and risks described may have shifted since publication.

The Bottom Line

LLMs can meaningfully supplement clinical education through case generation and feedback, but they are a tool for increasing practice opportunities, not a substitute for human mentorship and supervision.