Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: USE OF ARTIFICIAL INTELLIGENCE FOR THE EVALUATION OF THE ENTRANCE EXAMINATION OF THE BRAZILIAN SOCIETY OF SHOULDER AND ELBOW SURGERY

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.

Read original paperSearch paper agentCC BY 4.0

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:

ABSTRACT Introduction: AI is increasingly used for medical education and assessment, yet effectiveness on specialized exams remains underexplored. We aim to compare multiple AI models with national human averages on SBCOC entrance exams (2021–2023), evaluate answer accuracy and citation reliability, and contrast models. Methods: Five models—ChatGPT-4 (standard and literature-trained), ChatGPT-o1-pro, Gemini, and Meta Llama 3.1—answered official SBCOC exams (50 items/year) individually using a unified prompt and identical images. Each response included one choice (A–D) and a cited source. Outcomes were accuracy and source reliability (peer-reviewed articles/textbooks versus websites). Statistics used chi-square tests and one-way ANOVA with Tukey post-hoc (p<0.05). Results: Across 150 questions, ChatGPT-o1-pro led (66%, 62%, 68%) and exceeded human national averages each year (p<0.05). Gemini (40%, 28%, 38%) and Meta Llama 3.1 (32%, 44%, 50%) underperformed relative to humans, while both ChatGPT-4 versions hovered near the ≥50% pass threshold without significant differences. Regarding sources, ChatGPT-o1-pro and Llama predominantly cited articles or books, ChatGPT-4 alternated between literature and websites, and Gemini did not specify references. Conclusions: ChatGPT-o1-pro outperformed the national human average and mostly used credible sources; ChatGPT-4 matched humans, while Gemini and Meta Llama 3.1 lagged. Level of evidence IV; Case series.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Caio Henrique Kenchian; Karolina Stephany Pereira Ferreira; Lucas Pereira Sarmento; Marcelo Silveira Teixeira; Renato Aroca Zan; Marcel Jun Sugawara Tamaoki. USE OF ARTIFICIAL INTELLIGENCE FOR THE EVALUATION OF THE ENTRANCE EXAMINATION OF THE BRAZILIAN SOCIETY OF SHOULDER AND ELBOW SURGERY. Acta Ortopedica Brasileira (2026-07-17). https://doi.org/10.1590/1413-785220263404e300345

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.