Agentic education · Learning science

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Learning with agency: The Unique Contribution of Digital Maturity in the AI Era: Psychometric Validation, Predictive Ability and Latent Profile Analysis Among Chinese Primary Students

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.

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

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

With the widespread use of digital technologies, it has become increasingly important to understand how children adapt to digital environments. However, limited research has examined digital maturity among children beyond digital access and familiarity with technologies, particularly in non-Western contexts. Building on recent work on digital maturity, the present study aimed to validate the Digital Maturity Inventory (DIMI) among Chinese primary school students and to identify their latent profiles. A sample of 434 students (221 girls; Meanage = 10.68, SD = 0.86) completed the DIMI for validation analyses, and an additional sample was simultaneously recruited, resulting in a total sample of 1026 students (500 girls; Meanage = 10.38, SD = 1.09), for latent profile analysis. The findings demonstrated that the DIMI exhibited satisfactory psychometric properties in the Chinese context. Moreover, digital maturity, in addition to digital native, emerged as a unique and significant predictor of artificial intelligence (AI) literacy. Three distinct subgroups of students with heterogeneous patterns of digital maturity were also revealed: an unbalanced low group, an inconsistent medium group, and a relatively high group. These findings not only support the cross-cultural applicability of DIMI, but also demonstrate its relevance to children’s AI-related competencies. The implications of this study were also discussed.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Primary school; 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.

Study methods · source passage

Study 1 recruited 459 students from a public primary school in southern China using convenience sampling. To ensure data quality, two attention-check items requiring students to select “always” were included to identify potentially invalid responses. Questionnaires with failed attention checks were excluded from further analysis. No missing data were observed among the retained responses. Finally, 434 students (94.6% valid response rate; 221 girls; Meanage = 10.68, SD = 0.86) were retained for further analysis. All participants were asked to finish an online questionnaire at school, which took about 15 min. Informed consent was obtained from parents and schools, and all participants signed the consent form and were informed that they could withdraw from the study at any time without any negative consequences. To ensure data privacy and confidentiality, all responses were anonymized before analysis. The collected data were securely stored and were accessible only to the research team for research purposes. This study was conducted in accordance with the ethical guidelines of the authors’ university. In addition, students were also asked to report their average digital device use time on weekdays and weekends, respectively. The details of the demographic information are shown in Table 1.

Limitations and uncertainty · source passage

Despite these advances, several important research gaps remain. First, existing validation studies have been conducted primarily in Western samples (e.g., Austria, Germany, and The Netherlands), with limited evidence regarding whether digital maturity represents a culturally generalizable developmental construct among children. This limitation is particularly important in the Chinese context, where children’s digital engagement has increased rapidly in recent years. According to the CNNIC reports (CNNIC, 2021, 2024), Internet access among Chinese children increased from 89.5% in 2018 to 95.1% in 2022 and further reached 97.2% in 2023. Therefore, examining the psychometric properties and applicability of digital maturity among Chinese primary students can extend current understanding of children’s digital development across cultural contexts. Furthermore, although digital maturity emphasizes adaptive, self-regulatory, and responsible engagement with digital technologies, its relevance to emerging AI-related competencies remains insufficiently understood. Specifically, it remains unclear whether digital maturity explains unique differences in AI literacy beyond technology exposure and demographic characteristics.

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.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

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. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. 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. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. 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

Meng Sun; Jiutong Luo. The Unique Contribution of Digital Maturity in the AI Era: Psychometric Validation, Predictive Ability and Latent Profile Analysis Among Chinese Primary Students. Journal of Intelligence (2026-08-01). https://doi.org/10.3390/jintelligence14080172

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.