Actionable companion · Cognitive load & attention
Learning with agency: Influencing factors of smartphone addiction in the era of artificial intelligence: based on the perspective of home-school
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): Higher education, Children · stage unspecified. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
In the era of artificial intelligence, undergraduates have the characteristics of convenience and time flexibility in the use of smart phones, and their addiction is directly related to the sustainable development of students “physical and mental health. Family and school are the main fields of their life and learning, so exploring the dependence of undergraduate students on smartphones from the dual perspectives of home and school undoubtedly has important social responsibility significance. We use t-test, one-way analysis of variance and stepwise regression method to analyze the data of 556 undergraduates, and explore the differences of undergraduates” smartphone addiction, the influencing factors from the perspective of home-school and the interaction effect between home and school. The study shows that there are significant differences in gender, number of children in the family, and grade among undergraduates’ smartphone addiction. Social capital within and outside the family, family embodied and objective cultural capital, and student support have a significant negative impact on undergraduates “smartphone addiction. Family economic capital, family institutionalized capital, teacher support and independent opportunities have no significant impact on undergraduates” smartphone addiction. Social capital within and outside the family has a mutual substitution effect with student support, and family embodied and objective cultural capital has a mutual promotion effect with student support. Therefore, we should pay attention to the classification of smartphone addiction, make good use of family capital to regulate smartphone addiction, and create student support.
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
Stage: Higher education; 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.
Limitations and uncertainty · source passage
The article explored the influencing factors of undergraduate students’ smartphones from the dual perspective of home and school, and made some valuable conclusions, but there are still some limitations. First, the study obtained cross-sectional data through questionnaires, which is difficult to dynamically reflect the dynamic changes of undergraduates’ smartphone addiction. Future research can be based on a combination of cross-sectional and longitudinal studies to elucidate the dynamic changes of undergraduate smartphone addiction. Second, the factors affecting undergraduates’ smartphone addiction are diversified, and the article focuses on factors such as family capital and school climate, which to a certain extent misses some factors, and does not consider the mediating mechanism in exploring the influencing factors of undergraduates’ smartphone addiction. Finally, future research can further explore the mechanism of home-school synergy from multiple angles under the mechanism of home-school independence and collaboration, in order to clarify the specific aspects of home-school synergy.
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.
- Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
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. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. 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. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. 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
Jianxiang Fei; Yingyu Zhang. Influencing factors of smartphone addiction in the era of artificial intelligence: based on the perspective of home-school. Frontiers in Psychology (2026-05-08). https://doi.org/10.3389/fpsyg.2026.1770457
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.