Actionable companion · Governance, equity and accountability

From evidence to district action: A qualitative thematic comparative analysis of independent and AI-supported clinical reflections among medical interns

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

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

Background Reflection is fundamental to medical education, supporting clinical reasoning, professional identity formation, and lifelong learning. Although generative artificial intelligence (AI) chatbots have emerged as tools to facilitate reflection, little is known about how AI-supported reflection differs from independent reflective writing. This study compared the characteristics and educational contributions of independent reflective writing and AI-supported reflection among medical interns. Methods A comparative qualitative quasi-experimental study was conducted with medical interns during an emergency medicine rotation. The control group was instructed to perform reflective writing independently, while the intervention group was tasked to use AI-supported reflections using a purpose-built chatbot to complete reflective writing. Data were analyzed using qualitative content analysis and compared in terms of themes, reflective depth, clinical reasoning, emotional awareness, and professional learning. Results Twenty-one interns completed independent reflective writing, while eighteen engaged in AI-supported reflection using a chatbot. Although both approaches supported reflective learning, they facilitated distinct dimensions of the reflective process. Independent reflective writing was characterized by greater autonomous meaning-making, critical reflection, ethical reasoning, and systems-level thinking. In contrast, AI-supported reflection was characterized by greater emotional articulation, structured reflective dialogue, and future learning planning, but showed more limited exploration of contextual, ethical, and systems-level issues. Participants’ comments embedded within the chatbot interactions suggested that some found the interaction supportive, while repetitive prompting and limited critical exploration emerged as limitations. Conclusions Independent reflective writing and AI-supported reflections represent complementary rather than interchangeable educational approaches. While independent reflective writing fosters deeper critical reflection, AI primarily functions as a reflective scaffold that supports emotional engagement and structures reflective learning. Integrating AI-assisted reflections with independent writing and faculty-guided debriefing may optimize reflective learning in medical education.

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. Convene a teacher, student, family and technology working group. Test one proposed use against a documented privacy, accessibility, bias and human-review checklist before authorizing a pilot. 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. Track unresolved risks, subgroup access, complaint resolution time and the proportion of outputs that require substantive human correction. 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

Selçuk Akturan; Sinan Paslı; Salman Yousuf Guraya. A qualitative thematic comparative analysis of independent and AI-supported clinical reflections among medical interns. Frontiers in Medicine (2026-09-15). https://doi.org/10.3389/fmed.2026.1942949

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.