Actionable companion · Learning design & outcomes

Learning with agency: Development of artificially intelligent tool for analysis and prediction of myopia progression among school-going 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.

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

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

Abstract Background Myopia is a growing public health concern among school-going children, with increased screen time and reduced outdoor activities contributing to its early onset and rapid progression. In India, the absence of a standardized, population-based dataset reflecting socio-economic and environmental diversity limits the development of accurate prediction tools. This study aims to design and validate an artificial intelligence (AI)-based model to analyze and predict myopia progression among children aged 6 to 18 years in urban and rural areas. Methods This is a prospective, community-based study conducted in two phases across five districts of Bhopal division, India. Phase one involves a cross-sectional survey of approximately 5,000 school children to collect demographic and ocular health data, including visual acuity, refractive error, axial length, corneal parameters, and lifestyle factors. In phase two, a longitudinal cohort comprising 10% of the phase one participants (balanced for existing and non-existing myopia cases) will be followed every 6 months over 2 years. Machine learning algorithms, including linear regression, support vector machines, XGBoost, and deep learning models such as convolutional neural networks, will be trained on this dataset. An AI-based mobile application will be developed to enable field-level prediction and screening. Discussion This study will generate the first large-scale Indian dataset on myopia progression among children, incorporating diverse socio-economic backgrounds. The validated AI model and mobile application will support early identification of at-risk children, optimize resource allocation, and inform national screening strategies. By integrating real-time data analytics with field-l. Trial registration CTRI/2025/07/091243. Registered on 21/07/2025. Keywords: Myopia progression, School children, Artificial intelligence, Machine learning, Predictive modeling, Vision screening, Public health ophthalmology

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. Establish the participants’ ages, developmental needs and educational setting from the full paper before selecting an intervention. Use adult-mediated activity and preserve hands-on and peer learning. 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. Evaluate an age-appropriate independent task after the interaction. Do not infer a preschool or primary-school effect when the paper does not establish that population. 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

Priti Singh; Vidhya Verma; Samendra Karkhur; Arushi Beri; Jyoti Singhai; Dheraj Kumar Agrawal. Development of artificially intelligent tool for analysis and prediction of myopia progression among school-going children. Trials (2026-04-14). https://doi.org/10.1186/s13063-026-09648-w

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.