Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: Artificial intelligence model system for bone age assessment of preschool children

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.

Read original paperSearch paper agentCC BY 4.0

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): Early childhood · preschool, Children · stage unspecified. Verify the participant population and task before transferring the findings.

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

Abstract Backgroud Our study aimed to assess the impact of inter- and intra-observer variations when utilizing an artificial intelligence (AI) system for bone age assessment (BAA) of preschool children. Methods A retrospective study was conducted involving a total sample of 53 female individuals and 41 male individuals aged 3–6 years in China. Radiographs were assessed by four mid-level radiology reviewers using the TW3 and RUS–CHN methods. Bone age (BA) was analyzed in two separate situations, with/without the assistance of AI. Following a 4-week wash-out period, radiographs were reevaluated in the same manner. Accuracy metrics, the correlation coefficient (ICC)and Bland-Altman plots were employed. Results The accuracy of BAA by the reviewers was significantly improved with AI. The results of RMSE and MAE decreased in both methods (p < 0.001). When comparing inter-observer agreement in both methods and intra-observer reproducibility in two interpretations, the ICC results were improved with AI. The ICC values increased in both two interpretations for both methods and exceeded 0.99 with AI. Conclusion In the assessment of BA for preschool children, AI was found to be capable of reducing inter-observer variability and enhancing intra-observer reproducibility, which can be considered an important tool for clinical work by radiologists. Impact The RUS-CHN method is a special bone age method devised to be suitable for Chinese children. The preschool stage is a critical phase for children, marked by a high degree of variability that renders BA prediction challenging. The accuracy of BAA by the reviewers can be significantly improved with the aid of an AI model system. This study is the first to assess the impact of inter- and intra-observer variations when utilizing an AI model system for BAA of preschool children using both the TW3 and RUS-CHN methods.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Early childhood · preschool; Children · stage unspecified. 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.

Participant ages · source passage

Methods A retrospective study was conducted involving a total sample of 53 female individuals and 41 male individuals aged 3–6 years in China.

Study methods · source passage

This retrospective study involved the selection of 471 left hand-wrist radiographs sourced from the Third Affiliated Hospital of Zhejiang Chinese Medicine University in Hangzhou, China. The radiographs were all retrieved from the Picture Archive and Communication Systems (PACS) spanning the period between January 2018 and December 2022. The inclusion criteria stipulated: 1) children aged between 3 and 6 years, 2) children of Chinese nationality and residing in China, and 3) children with no significant medical history of conditions affecting skeletal development. Unclear left hand-wrist radiographs were excluded from the study. We performed stratified random sampling among preschool children aged 3–6, with 20% of the data from each age group included in the study sample. This resulted in a total of 53 female individuals and 41 male individuals (Fig.1, Table 1). None of the cases in the study were involved in the training and validation of the AI model system.

Limitations and uncertainty · source passage

The present study has several limitations: 1) This is a single-center, cross-sectional study with a small sample size, focused only on a specific population aged 3–6 years in China. 2) The study exclusively compared the TW3 and RUS-CHN methods, but other methods like the GP method, which is commonly used in various regions and hospitals, were not considered. 3) The observers in this study were mid-level attending physicians, and there was no comparison with physicians of other levels, such as junior and senior physicians. 4) The timing of bone age assessment was not documented, even though previous studies have found that AI can reduce assessment time. Comparative time consumption should be considered. Therefore, more in-depth multicenter studies are necessary to validate these findings, incorporating various BAA methods and observers with different levels of experience in future research.

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. Use a brief adult-mediated story, turn-taking or language activity. An educator controls every interaction; the agent never becomes an unsupervised companion. Preserve hands-on play and human conversation. 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. Measure independent naming, explanation or turn-taking in a human-led activity after the tool is removed. Record frustration, overstimulation and the amount of adult help. 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

Chengcheng Gao; Chunfeng Hu; Qi Qian; Yangsheng Li; Xiaowei Xing; Ping Gong; Min Lin; Zhongxiang Ding. Artificial intelligence model system for bone age assessment of preschool children. Pediatric Research (2024-05-27). https://doi.org/10.1038/s41390-024-03282-5

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.