Suuri kielellinen malli fysioterapian opiskelijoiden kliiniselle reagoinnille

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.

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.

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.

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.

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.

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)

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.

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.