Large Linguistic Model for Clinical Reaoning of Physical Therapy Students
Trial statusRecruiting
Trial phasePhase 2
Trial typeInterventional
Biological sexAll
Age18-30
SponsorNeuron, Spain
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial design
Design model
Parallel
Treatments tested in this trial
Large Language Model
Other interventionThe intervention in the experimental group is distinguished by the integration of a Large Language Model (LLM)-based interactive platform (ChatGPT) into clinical reasoning training for physical therapy students. Unlike traditional educational approaches, this intervention provides real-time, AI-generated patient interactions, allowing students to actively engage in virtual clinical case simulations.
Conventional
Other interventionThe intervention in the control group follows a traditional case-based learning approach, which is commonly used in physical therapy education. Unlike the experimental group, this training method relies solely on human-led instruction and written case analysis, without the integration of artificial intelligence or interactive digital tools.
Treatment groups
60 Participants
are divided into 2 treatment groupsGroup A: LLM groupExperimental treatment 1 intervention
Group B: Conventional learning groupActive comparator 1 intervention
Trial outcomes
Primary outcomes
1
Clinical Reasoning Performance
This outcome measures the improvement in students' clinical reasoning skills after the intervention. Students will be assessed based on their ability to collect, interpret, and analyze patient information, and formulate accurate diagnoses and treatment plans. This will be evaluated through both written case studies and practical exams using the Lasater rubric, being this scale the instrument used for evaluating this outcome.
Time frame
Assessed at the beginning and end of the 8-week intervention through case-based assessments and practical evaluations.Secondary outcomes
1
Digital competences
This outcome evaluates the students' ability to effectively use digital tools, particularly AI-driven platforms like ChatGPT, in the context of clinical reasoning. Students will complete a digital competence questionnaire assessing their skills across several domains, such as data management, health communication, and digital content creation. It will be assessed with the Digital competences questionnaire developed by Montero-Delgado et al. (2020)
Time frame
Evaluated at the start and end of the 8-week intervention via the ad hoc digital competence questionnaire.2
Student engagement with the intervention
This outcome measures the level of active engagement of students with the LLM-based platform (experimental group) and the traditional case-based learning (control group). Engagement will be assessed based on the frequency of interactions, duration of usage, and completion rates of the assigned cases.
Time frame
Monitored throughout the 8-week intervention period with weekly tracking of student interactions and case completions.3
Satisfaction with the educational approach
This outcome assesses overall student satisfaction with their learning experience, focusing on the effectiveness, usability, and perceived value of the intervention. Satisfaction will be measured using a Visual Analog Scale (VAS), where students rate their level of satisfaction with the training method.
Time frame
Calculated at the end of the intervention period, using the costs associated with providing access to the LLM-based platform and comparing it to the improvements observed in other outcomes.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Neuron, Spain
Lead sponsor
Centro Universitario La Salle
Sponsor institution
Source ClinicalTrials.gov