Physical Therapy Education

This study evaluates whether large language models (LLMs) can reliably assess written clinical-reasoning case examinations completed by undergraduate physiotherapy students, compared with faculty assessment. In the course "Specific Methods in Physiotherapy" (third year of the Physiotherapy Degree), students solve complex clinical cases that require clinical reasoning, technical knowledge, and therapeutic decision-making. These cases are traditionally graded by faculty, a time-consuming process that may show inter-rater variability. A set of de-identified student case examinations will be assessed using the rubric currently applied in the course, which covers clarity and structure of clinical reasoning, integration of the biopsychosocial model (ICF and APTA frameworks), accuracy in identifying pain mechanisms, coherence between diagnosis, hypotheses, and treatment, originality and depth of analysis, and professional writing. Each examination will be scored independently by three LLMs (for example, Claude, ChatGPT, and Gemini), each receiving an identical standardized prompt that embeds the same rubric, and by faculty serving as the reference standard. To avoid overloading faculty, full double human grading may not be feasible; the human reference will therefore consist of expert faculty grading by one independent rater or, when resources allow, two independent raters. In contrast, paired assessment is fully implemented across the AI models: each examination is scored by several LLMs, and each model is queried in duplicate, allowing the study to estimate agreement between models and the test-retest stability of each model. The primary aim is to quantify agreement between LLM-generated scores and the faculty reference score. Secondary aims include agreement among the LLMs, test-retest reliability of each model, criterion-level agreement, the quality and usefulness of the qualitative feedback generated, the time and cost associated with each approach, and students' perceptions of the usefulness of human versus AI feedback. The findings will clarify the strengths and limitations of LLMs as supportive tools for formative assessment in health-professions education and will inform criteria for their responsible and effective use. No LLM output will affect students' official grades, which remain the sole responsibility of faculty.

This study examines whether physical therapy faculty can reliably and feasibly use the Lasater Clinical Judgment Rubric adapted for physical therapy (LCJR-PT) to score students' clinical judgment during a recorded simulation experience. Multiple trained faculty raters will independently score the same student videos, and the level of agreement between raters will be measured. Faculty will also report how long scoring takes and their perceptions of the rubric's usefulness.

In the modern era, university students, especially those from Generation Z, are true digital natives who have grown up with technology and embraced it wholeheartedly. This hyper-cognitive generation has different characteristics than their predecessors and emphasizes educational approaches integrated with innovative technologies over traditional methods. Rather than traditional classroom learning, Generation Z prefers to learn through various media, such as digital games, YouTube videos, and smart devices. Science and technology have made rapid and transformative progress in recent decades. One of these developments is the emergence of the metaverse, a rapidly expanding three-dimensional virtual space. Neal Stephenson coined the term "metaverse" in his 1992 science fiction novel Snow Crash, combining the words "meta," meaning "transcendence and virtuality," and "verse," meaning "world and universe." Enhanced by technologies such as augmented reality (AR), virtual reality (VR), and artificial intelligence, the metaverse creates a 3D virtual sphere where individuals from around the world can come together for shared social interactions. It also serves as an arena for users to participate in various social activities. In recent years, significant events such as the pandemic, conflicts, seismic activity, and climate change have led to disruptions in higher education in certain countries. Due to the pandemic, there has been a significant shift towards distance learning platforms, and students have been denied various opportunities. Distance learning is defined as "an effective form of learning that is independent of time and place. It offers individuals the ability to conveniently configure and update educational materials electronically and integrate different technologies into the learning process. These technologies can be utilized 24/7." The goal of distance education is to ensure continuity of education and increase learner satisfaction. Consequently, universities have adopted various distance learning models to address these challenges and promote their students' academic success. Using the metaverse in education is a significant application area and is widely anticipated to be very promising in the future.