Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Artificial Intelligence in Health Professions Education: Qualitative Study of Student Experiences

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:

Abstract Background Artificial intelligence (AI) is increasingly integrated into education and health care, raising questions about how students use these technologies and how AI influences their learning. In health education, understanding these trends is particularly important because student learning directly impacts future clinical skills. Objective This study aimed to explore the use of AI tools by health sciences students at the University of Ottawa. More specifically, it sought to identify the most frequently used AI tools, describe students’ usage habits, determine which tools support knowledge acquisition and skill development, and gather students’ recommendations for effective strategies to raise awareness and train their peers on the responsible use of AI. Methods A qualitative approach was used with students from 10 health professions who reported using AI in their studies. Data were collected through semistructured interviews and an open-ended qualitative online survey. Inductive thematic analysis within an interpretive paradigm was applied to capture patterns, perceptions, and emergent themes. Results A total of 51 health professions students participated in the study. Most were women between the ages of 20 and 29 years. ChatGPT (OpenAI) emerged as the most frequently used AI tool. Students perceived AI as a complementary tool that facilitated knowledge acquisition, skill development, writing, and problem-solving. AI adoption was driven by curiosity, peer influence, and the desire to improve work efficiency. Students critically evaluated AI results, integrated the tools into their learning processes, and emphasized the importance of technical skills, critical thinking, and digital literacy. Peer learning, hands-on demonstrations, and access to online resources were recommended for effective AI training. Conclusions This research demonstrates that health professions students actively use AI tools, particularly ChatGPT, to support learning, skill development, and academic tasks. Although AI is valuable as an educational aid and its use varies by student and context, this highlights the need for structured guidance, critical evaluation skills, and peer-supported training. These findings highlight the importance of thoughtfully integrating AI into educational programs to enhance learning outcomes, foster skill acquisition, and ensure responsible and effective adoption.

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

An inductive thematic analysis approach, following an interpretive research paradigm, was used to identify the AI platforms most used by health professions students during their learning activities. This approach was chosen for its relevance to explore perceptions and experiences regarding AI tools in medical education [18]. This approach, based on the interpretive paradigm, posits that participants’ narratives construct meanings, which promotes a detailed understanding of behaviors and points of view [19]. Therefore, in accordance with the research objectives, the inductive approach facilitated the emergence of themes directly from the data for a more flexible and data-driven interpretation [20]. This paper was written by using the Standards for Reporting Qualitative Research (SRQR) guide (Checklist 1) [21].

Limitations and uncertainty · source passage

One of the limitations of this study is the small sample size and the use of self-reported data, and the fact that the data were collected within a single establishment, which limits the generalization of the results to other institutions. This may have introduced biases related to memorization accuracy or social desirability. Although the research team assessed the questionnaire for relevance and clarity, psychometric procedures were not sufficiently applied to assess it. Therefore, interpretation biases may arise, particularly regarding the understanding of porous concepts such as “learning” or “learning support.” Furthermore, health professions students were the focus of this study; the application of AI in training may vary by discipline. In addition, this transition in data collection, from semistructured interviews to online surveys, may have reduced the depth and richness of some responses, which could affect the identification of more subtle themes.

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.

  • 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

Marina Guirguis; Salomon Fotsing; Joanne Fevry; Christine Landry; Diane Bouchard-Lamothe; Jennifer Lacroix; Alireza Jalali. Artificial Intelligence in Health Professions Education: Qualitative Study of Student Experiences. Journal of Medical Internet Research (2026-04-02). https://doi.org/10.2196/82432

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.