Actionable companion · Assessment & reasoning
Learning with agency: AI-based color vision screening and educational counseling for high school students: An experimental study
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): Secondary school. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Color vision deficiency (CVD) can negatively affect students’ learning experiences and career choices; however, combined screening and educational counseling remain challenging in school settings, particularly in low-resource contexts. Conventional paper-based color vision tests are time-consuming, difficult to standardize, and rarely linked to individualized educational or career guidance. This study aimed to design, implement, and validate an Android-based mobile application that integrates CVD screening with artificial intelligence (AI)–assisted educational and career counseling for high school students. A cross-sectional study was conducted with 527 high school students in Danang, Viet Nam. The application incorporated a digital Ishihara-based screening module and an AI-assisted counseling component. Screening outcomes were compared with conventional Ishihara testing to evaluate agreement, while students’ perceptions of usability, usefulness, and counseling relevance were assessed using a validated questionnaire. Statistical analyses included descriptive statistics, independent-samples t-tests, reliability analysis using Cronbach’s alpha, and exploratory factor analysis. Seventeen students (3.23%) were identified as having CVD, including red-green, protan, deutan, and achromatopsia (complete CVD). The mobile application demonstrated high concordance with conventional screening results. No statistically significant differences were observed between students with and without CVD in overall system evaluation scores (p > 0.05). The questionnaire showed good internal consistency (Cronbach’s alpha = 0.857) and a clear multi-factor structure reflecting students’ acceptance of AI-assisted educational and career counseling. These findings indicate that the proposed mHealth application offers a feasible and scalable approach for school-based CVD screening combined with personalized educational and career guidance. The system may serve as a supportive decision-making tool for students and counselors, while further validation under diverse real-world conditions and across different mobile devices is recommended.
What kind of evidence is this?
Supporting AI learning science · Survey / observational study
Stage: Secondary school. 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.
Study methods · source passage
The color vision screening application was developed as a mobile-based system for use on the Android platform, with a focus on usability, accessibility, and scalability in real-world educational and community settings. Although the application is designed to be applicable to users across different age groups, the present study evaluated its implementation and effectiveness among high school students. The user interface was developed to be simple and intuitive, ensuring ease of use for both adolescent and adult users.
Limitations and uncertainty · source passage
Beyond screening technologies, AI has also been increasingly explored as a tool for enhancing educational and career guidance for high school students. AI-assisted counseling systems enable data-driven personalization, facilitate access to career-related information, and help overcome structural limitations of traditional counseling approaches. Evidence from the Vietnamese educational context suggests that students with specific needs, including those with perceptual or visual limitations such as CVD, demonstrate a higher demand for and trust in AI-supported guidance systems. These insights indicate that combining early screening with AI-assisted educational counseling may provide a more inclusive and personalized support framework within school settings [10].
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
- 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.
- Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- 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.
- Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. 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
Dang Vinh; Phan Thi Yen. AI-based color vision screening and educational counseling for high school students: An experimental study. PLOS One (2026-07-22). https://doi.org/10.1371/journal.pone.0353871
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.