Actively Recruiting
Large Linguistic Model for Clinical Reaoning of Physical Therapy Students
Led by Neuron, Spain · Updated on 2025-12-04
60
Participants Needed
1
Research Sites
151 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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: 1. 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. 2. 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.
CONDITIONS
Official Title
Large Linguistic Model for Clinical Reaoning of Physical Therapy Students
Who Can Participate
Eligibility Criteria
You may qualify if you...
- 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 by signing informed consent after being informed about the study
- Participants must be willing to engage with the LLM-based platform or traditional learning activities for the study duration
You will not qualify if you...
- Students with previous clinical experience beyond the third year of physiotherapy education
- Physical or cognitive disabilities that may interfere with participation or benefit from the intervention (e.g., vision, hearing, or motor impairments)
- Students who do not provide informed consent to participate
- Students without sufficient proficiency in Spanish or English to understand materials and the intervention
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Centro Superior de Estudios Universitarios La Salle
Madrid, Madrid, Spain, 28023
Actively Recruiting
Research Team
A
Alfredo Lerín Calvo, Professor
CONTACT
How is the study designed?
Study Type
INTERVENTIONAL
Masking
DOUBLE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
TREATMENT
Number of Arms
2
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