Feasibility of a Randomized Controlled Trial of Large Artificial Intelligence-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students. A Randomized Controlled Trial
Trial ID: NCT06809634
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: PHASE2
Start date: 2023-09-01
Completion date: 2026-07-30
Sponsor: Neuron, Spain
Conditions: Healthy; Artificial Intelligence (AI)
Interventions: Large Language Model; Conventional
Locations: Centro Superior de Estudios Universitarios La Salle
Countries: Spain
Enrollment: 60
Age range: 18 Years - 30 Years
Sex: ALL

Summary:
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:
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.

Outcomes:
Clinical Reasoning Performance