Actionable companion · Feedback & scaffolding

From AI evidence to responsible practice: Artificial Intelligence for Assessment and Feedback in Medical Education: Bibliometric Mapping Study and Thematic Evidence Map

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

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. Health-professions education findings do not establish effectiveness in K–12 classrooms.

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

Background Artificial intelligence (AI), particularly generative AI and large language models, is increasingly used for assessment-related tasks in medical education. Existing overviews often address AI in medical education broadly, limiting assessment-specific interpretation of functions, settings, learner stages, and responsible-AI reporting domains. Objective This study aims to map the literature on AI for assessment and feedback in medical education, including publication trends, bibliometric structure, assessment functions, AI types, settings, learner stages, and reporting of validity, reliability, fairness, integrity, transparency, human oversight, implementation, and governance. Methods We conducted a bibliometric mapping study incorporating structured thematic evidence-map coding. Web of Science Core Collection, Scopus, and PubMed were searched from January 1, 2015, to April 8, 2026. Document selection and main evidence-map coding were based primarily on titles, abstracts, and bibliographic metadata, with targeted ambiguity resolution. Because reporting domains may appear mainly in full-text sections, all 435 included records underwent full-text sensitivity analysis for the 8 reporting domains. Coding reliability was assessed by two coders using percent agreement and Cohen κ before adjudication. Exploratory subgroup analyses and an excluding-2026 partial-year sensitivity analysis were conducted. Results Searches identified 14,968 records; 435 were included after deduplication and selection. Overall, 399 (91.7%) records were indexed in the post-ChatGPT period. Generative AI was coded in 310 (71.3%) records, and large language models in 301 (69.2%) records. In the assessment-function umbrella analysis, learner performance evaluation accounted for 270 (62.1%) records, feedback for 93 (21.4%) records, assessment content generation for 65 (14.9%) records, and other or unclear functions for 7 (1.6%) records. The most common settings were board-style examinations (n=151, 34.7%) and written examinations (n=88, 20.2%); undergraduate medical education was the most represented learner stage (n=172, 39.5%). Full-text–confirmed reporting was most frequent for reliability (n=288, 66.2%) and implementation (n=231, 53.1%); intermediate for validity (n=158, 36.3%), fairness (n=132, 30.3%), and transparency (n=130, 29.9%); and less frequent for governance (n=57, 13.1%), human oversight (n=46, 10.6%), and integrity (n=26, 6%). Stage 1 κ values ranged from 0.785 to 0.895, and stage 2 κ values ranged from 0.809 to 0.880. Excluding 65 partial-year 2026 records did not change the overall interpretation. Conclusions The indexed English-language literature on AI for assessment and feedback in medical education expanded rapidly in the post-ChatGPT period and was concentrated in generative AI, large language models, examination-oriented assessment, and undergraduate medical education. Future studies should complement examination benchmarking with authentic assessment contexts, distinguish assessment content generation from learner-facing evaluation and feedback, and report responsible assessment domains more consistently.

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

Zihang Zhao; Zihan Liu; Liang Guo; Teng Pan; Yousheng Zhang; Chenxiang Miao; Yiting Ge; Yipeng Wang; Xin Hu; Xin Wang; Ruipeng Zhang; Zhiyong Hou. Artificial Intelligence for Assessment and Feedback in Medical Education: Bibliometric Mapping Study and Thematic Evidence Map. JMIR Medical Education (2026-07-02). https://doi.org/10.2196/98949

Companion prepared 2026-11-01. 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.