Large Linguistic Model for Clinical Reaoning of Physical Therapy Students

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.

Conditions

• Healthy
• Artificial Intelligence (AI)

Interventions

OTHER

Large Language Model

The 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.

OTHER

Conventional

The 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.

Sponsors & Collaborators

• Neuron, Spain

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

30 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2023-09-01

Primary Completion

2026-06-30

Completion

2026-07-30

Countries

• Spain

Study Locations