Large Language Models Versus Human Examiners for Grading Physiotherapy Clinical Cases

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Conditions studied: Educational Assessment, Artifical Intelligence, Physical Therapy Education

In brief

This study evaluates whether large language models (LLMs) can reliably assess written clinical-reasoning case examinations completed by undergraduate physiotherapy students, compared with faculty assessment. In the course "Specific Methods in Physiotherapy" (third year of the Physiotherapy Degree), students solve complex clinical cases that require clinical reasoning, technical knowledge, and therapeutic decision-making. These cases are traditionally graded by faculty, a time-consuming process that may show inter-rater variability. A set of de-identified student case examinations will be assessed using the rubric currently applied in the course, which covers clarity and structure of clinical reasoning, integration of the biopsychosocial model (ICF and APTA frameworks), accuracy in identifying pain mechanisms, coherence between diagnosis, hypotheses, and treatment, originality and depth of analysis, and professional writing. Each examination will be scored independently by three LLMs (for example, Claude, ChatGPT, and Gemini), each receiving an identical standardized prompt that embeds the same rubric, and by faculty serving as the reference standard. To avoid overloading faculty, full double human grading may not be feasible; the human reference will therefore consist of expert faculty grading by one independent rater or, when resources allow, two independent raters. In contrast, paired assessment is fully implemented across the AI models: each examination is scored by several LLMs, and each model is queried in duplicate, allowing the study to estimate agreement between models and the test-retest stability of each model. The primary aim is to quantify agreement between LLM-generated scores and the faculty reference score. Secondary aims include agreement among the LLMs, test-retest reliability of each model, criterion-level agreement, the quality and usefulness of the qualitative feedback generated, the time and cost associated with each approach, and students' perceptions of the usefulness of human versus AI feedback. The findings will clarify the strengths and limitations of LLMs as supportive tools for formative assessment in health-professions education and will inform criteria for their responsible and effective use. No LLM output will affect students' official grades, which remain the sole responsibility of faculty.

Key facts

Study ID

NCT07677202

Run by

Neuron, Spain

People needed

65

Starts

2026-08-01

Expected to finish

2026-08-10

Last updated by the study team

2026-06-30

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• Students officially enrolled in the course "Specific Methods in Physiotherapy" (third year of the Physiotherapy Degree) during the study period.
• Submission of a completed written clinical-reasoning case examination as part of the course.
• Provision of informed consent for the anonymized examination to be used for educational-research purposes.

You may not qualify if…

• Refusal to provide, or withdrawal of, informed consent.
• Blank, incomplete, or non-evaluable examinations (e.g., no developed written response).
• Examinations that cannot be reliably de-identified prior to assessment.

Where it is running

• Centro Superior de Estudios Universitarios La Salle — Madrid, Madrid, Spain

Full record on ClinicalTrials.gov

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