Overview
Clinical reasoning is a fundamental skill for physical therapy students, enabling them to collect and interpret patient information to make accurate diagnoses and treatment decisions. Traditional training methods often limit students' exposure to a diverse range of clinical cases, which can restrict the development of these skills. The integration of Large Language Models (LLMs), such as ChatGPT, into physical therapy education offers a novel approach to enhance clinical reasoning by simulating interactive and realistic patient scenarios.
This randomized controlled trial aims to evaluate the effectiveness of an LLM-based educational intervention in improving clinical reasoning skills in physical therapy students. The study will recruit a total of 200 third-year physiotherapy students from multiple university institutions. Participants will be randomly assigned to one of two groups:
- Experimental Group - Students will receive LLM-based training, engaging with a conversational artificial intelligence model to solve clinical cases over an 8-week period. The model will provide real-time responses to their questions, allowing them to refine their diagnostic and treatment reasoning.
- Control Group - Students will follow the standard curriculum, participating in conventional case-based learning and supervised clinical reasoning exercises without AI-based assistance.
The primary outcome of the study is the improvement in clinical reasoning skills, assessed through standardized written case evaluations and structured practical examinations. Secondary outcomes include changes in digital competence, student engagement levels, overall satisfaction with the educational approach, and cost-effectiveness of the intervention.
By assessing the impact of LLMs on clinical reasoning training, this study seeks to determine whether AI-driven educational tools can effectively complement traditional physiotherapy education and improve student preparedness for real-world clinical practice.
Description
Clinical reasoning is a key competency for physical therapy students, allowing them to assess, diagnose, and create treatment plans based on patient information. Despite its importance, traditional educational approaches often limit students' exposure to a broad variety of clinical cases, restricting their ability to develop comprehensive reasoning skills. Advances in artificial intelligence, particularly Large Language Models (LLMs) such as ChatGPT, offer a promising solution by simulating realistic and interactive clinical scenarios.
This randomized controlled trial (RCT) aims to evaluate the effectiveness of an LLM-based intervention compared to traditional training methods in improving clinical reasoning skills among physical therapy students. The third-year students will be randomly assigned to either the experimental group, receiving AI-driven case-based training, or the control group, following conventional curriculum-based case discussions.
The intervention will last 8 weeks, during which students in the experimental group will interact with an LLM to solve weekly clinical cases, mimicking real-world patient encounters. The model will function as a virtual patient, responding to students' inquiries and allowing them to refine their diagnostic reasoning and treatment planning. In contrast, the control group will participate in traditional written and tutor-led case discussions.
Statistical Analysis Plan
Data will be analyzed using SPSS version 29.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics will be used to summarize baseline characteristics of participants, with continuous variables expressed as mean ± standard deviation (SD) or median [interquartile range], depending on normality, and categorical variables presented as frequency (n) and percentage (%). Normality of distributions will be assessed using the Kolmogorov-Smirnov test and Shapiro-Wilk test. Between-group comparisons will be performed using; Independent t-tests or Mann-Whitney U tests for continuous variables; Chi-square tests or Fisher's exact test for categorical variables; Repeated-measures ANOVA or linear mixed models will be used to evaluate changes over time in clinical reasoning scores, digital competence, and satisfaction levels.
Logistic regression models will be applied to explore predictors of engagement with the LLM-based intervention. Effect sizes (Cohen's d, Rosenthal's r) will be calculated to measure the magnitude of differences observed. A cost-effectiveness analysis will be conducted by comparing the cost of implementing the LLM-based intervention with the improvement in clinical reasoning scores and student engagement levels.
Statistical significance will be set at p < 0.05, and all analyses will be conducted using a two-tailed approach.
Eligibility
Inclusion Criteria:
- Students enrolled in the third year of the Physiotherapy program at La Salle Centre for Higher University Studies (LCHUS)
- Participants must be between 18 and 30 years old.
- Students must agree to participate in the study by signing an informed consent form after being briefed about the study's objectives, procedures, and potential risks.
- Participants must be willing to engage with the LLM-based platform (for the experimental group) or participate in traditional learning activities (for the control group) for the duration of the study.
Exclusion Criteria:
- Students with previous clinical experience beyond the third year of physiotherapy education.
- Physical or cognitive disabilities that may interfere with the ability to participate in or benefit from the intervention (e.g., vision, hearing, or motor impairments).
- Students who do not provide informed consent to participate in the study.
- Students who do not possess sufficient proficiency in Spanish or English to understand the materials and the intervention.