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
试验速览
- 阶段
- 2 期
- 状态
- 招募中
- 发起方
- 入组人数
- 60
- 试验地点
- 2
- 主要终点
- Clinical Reasoning Performance
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- Double (Investigator, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 30 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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.
研究组 & 干预措施
LLM group
Participants in the experimental group will undergo an 8-week intervention incorporating Large Language Model (LLM)-based training into their clinical reasoning education.
Students will engage in weekly clinical case simulations using an LLM-powered platform (ChatGPT), where they will interact with the model to obtain patient information, formulate diagnoses, and propose treatment plans. The LLM will provide real-time responses, simulating a virtual patient encounter.
The training will complement the standard curriculum, allowing students to practice clinical reasoning skills in a structured and interactive AI-assisted environment. At the end of the intervention, participants will complete a final case-based assessment to evaluate improvements in clinical reasoning, digital competence, and engagement with the technology.
干预措施: Large Language Model (Other)
Conventional learning group
Participants in the control group will follow the standard curriculum for clinical reasoning training over an 8-week period, without exposure to the LLM-based intervention.
Students will engage in weekly case-based discussions using traditional learning methods, including written case analyses and supervised discussions with instructors. These sessions will follow the usual educational framework used in physical therapy training programs, emphasizing diagnostic reasoning and treatment planning through instructor-led guidance.
At the end of the training period, participants will complete a final case-based assessment to evaluate their clinical reasoning skills, digital competence, and overall engagement with the learning process.
干预措施: Conventional (Other)
结局指标
主要结局
Clinical Reasoning Performance
时间窗: Assessed at the beginning and end of the 8-week intervention through case-based assessments and practical evaluations.
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.
次要结局
- Digital competences(Evaluated at the start and end of the 8-week intervention via the ad hoc digital competence questionnaire.)
- Satisfaction with the educational approach(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.)
- Student engagement with the intervention(Monitored throughout the 8-week intervention period with weekly tracking of student interactions and case completions.)
研究者
Alfredo Lerín Calvo
Professor
Centro Universitario La Salle