Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Background Generative Artificial Intelligence (GAI) has significantly impacted education at all levels, including health professional education. Understanding students’ experiences is essential to enhancing AI literacy, adapting education to GAI, and implementing GAI technology. Therefore, the aim was to explore physiotherapy students’ experiences of and thoughts on GAI in their education, and its potential implications for their future careers in healthcare. Methods Qualitative descriptive design. Focus groups were conducted, using a semi-structured interview guide, at the Physiotherapy program at Linköping University, Sweden, from March to April 2024. The 15 students were organized into three focus groups, one for each education year. The data was analyzed using inductive content analysis. Results An overarching theme “GAI—Great potential if navigating the challenges” emerged from three categories: 1) “Areas of GAI use in the learning process”: Students viewed GAI as a tool for introduction and inspiration, assimilating course content and enhancing clinical reasoning and problem-solving; 2) “Optimizing GAI use in education”: Students found GAI to be timesaving, tailored, and as a virtual study partner and teacher. They discussed the pros and cons of learning, concerns on permitted GAI usage, the need for a critical approach, and how individual experiences and interests influenced their interactions with GAI; 3) “Future with GAI in education and profession”: Students believed future GAI would be more reliable, use subject-specific GAI models and enhance health care delivery, but also pose risks related to profit motives and knowledge gaps. Conclusion Physiotherapy students found GAI beneficial for learning and clinical reasoning but expressed concerns about its impact on learning quality. They emphasized the importance of a critical approach when using GAI and the need for organizational support, including supporting permitted GAI use. Students believed that future advanced GAI models could provide accurate and reliable educational tools and healthcare tools supporting documentation and evidence-based decision-making. However, potential risks include business profit motives and knowledge gaps. Navigating these challenges is essential to fully leveraging GAI’s benefits in education and physiotherapy practice. Therefore, fostering a critical approach and ensuring robust organizational support is crucial for maximizing the positive impact of GAI in physiotherapy. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-025-07106-w.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

We employed a qualitative descriptive design. The data was collected through focus group interviews and analysed using inductive content analysis, to gain insights into the informants’ experiences of the phenomenon [25]. The COnsolidated criteria for REporting Qualitative research (COREQ) checklist [26] was applied.

Limitations and uncertainty · source passage

This study is part of ongoing efforts to conduct educational adaptations in relation to GAI and enhance AI literacy in students and teachers within the Physiotherapy program at Linköping University, Sweden. The students’ experiences and thoughts on GAI in their education provide valuable insights into implementing updated learning activities. Focus groups capture diverse experiences [25, 27], and a series of focus groups increase confidence in the emerging patterns [25]. Group dynamics could influence the conversation, so participants were instructed not to interrupt each other and were asked if they had anything more to add after each question. This approach fostered a good discussion climate and often elicited additional information.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Yvonne Lindbäck; Karin Schröder; Torkel Engström; Karin Valeskog; Sofi Sonesson. Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study. BMC Medical Education (2025-04-24). https://doi.org/10.1186/s12909-025-07106-w

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.