Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: Influence of Artificial Intelligence in Education on Adolescents’ Social Adaptability: A Machine Learning 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.

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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): Secondary school. Verify the participant population and task before transferring the findings.

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

Abstract This study aimed to investigate the influence of artificial intelligence in education (AIEd) on adolescents’ social adaptability, as well as to identify the relevant psychosocial factors that can predict adolescents’ social adaptability. A total of 1328 participants (meanage = 13.89, SD = 2.22) completed the survey. A machine-learning algorithm was used to find out whether AIEd may influence adolescents’ social adaptability as well as the relevant psychosocial variables, such as teacher–student relations, peer relations, interparental relations, and loneliness that may be significantly related to social adaptability. Results showed that it has a positive influence of AIEd on adolescents’ social adaptability. In addition, the four most important factors in the prediction of social adaptability among AI group students are interpersonal relationships, peer relations, academic emotion, and loneliness. A high level of interpersonal relationships and peer relations can predict a high level of social adaptability among the AI group students, while a high level of academic emotion and loneliness can predict a low level of social adaptability. Overall, the findings highlight the need to focus interventions according to the relation between these psychosocial factors and social adaptability in order to increase the positive influence of AIEd and promote the development of social adaptability. Keywords: artificial intelligence in education, adolescent, social adaptability, machine learning

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 participants in this study were recruited from 13 AI demonstration schools in Guangdong province, southern China, using random sampling. These 13 schools have AI classes with AI teaching experience and non-AI classes without AI teaching experience, and the AI classes of these 13 schools have AI teaching experience for one year or more. A total of 1338 participants participated in the study. People who meet the following criteria are not eligible for the study: unable to understand the terms in the questionnaire and leaving more than 30% of items uncompleted. In addition, the missing values of the included data will be replaced with averages. Therefore, sample comprised a total of 1328 adolescents (53.01% male), ranging in age from 8 to 19 years (meanage = 13.89, SD = 2.22), giving an effective return rate of 99.25%. Before completing the survey, all the participants gave written informed consent. Then, both AI group and non-AI group are required to complete the questionnaires at one time within 40 min.

Limitations and uncertainty · source passage

Adolescents are the principal recipients of AIEd and they are in a state that is very easily affected by the external environment [18,19,20]. However, previous studies only discussed the influence of AIEd on adolescents at the theoretical level, which may lead to some limitations. Considering the bidirectional influence of AIEd, we put forward:

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. 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.
  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 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

Chuyin Xie; Minhua Ruan; Ping Lin; Zheng Wang; Tinghong Lai; Ying Xie; Shimin Fu; Hong Lu. Influence of Artificial Intelligence in Education on Adolescents’ Social Adaptability: A Machine Learning Study. International Journal of Environmental Research and Public Health (2022-06-27). https://doi.org/10.3390/ijerph19137890

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.