Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: Pre-Service Teachers’ Perceptions of AI Technological Support and School-Based Intelligent Environment Support: Model-Based Indirect Associations with Innovative Competence via AI Self-Efficacy and Self-Regulated Learning

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Abstract As artificial intelligence (AI) becomes deeply integrated into teacher education, pre-service teachers’ innovative competence has become a cornerstone for fostering human–AI collaborative innovation and enabling intelligent educational transformation. This study used empirical data from 3003 pre-service teachers across 12 provinces in China. It employed difference tests, quantile regression, and structural equation modeling to examine the associations of AI technological support and school-based intelligent environment support with pre-service teachers’ innovative competence. Results showed that pre-service teachers’ innovative competence was at a medium-to-high level, with significant heterogeneity across individual, family, and school background variables. Both supports were positively associated with innovative competence. However, the patterns of association differed. The association between AI technological support and innovative competence generally increased across the middle-to-upper quantiles, whereas the association between school-based intelligent environment support and innovative competence was stronger at the lower-to-middle quantiles and subsequently declined. AI self-efficacy and self-regulated learning both showed significant model-based indirect associations linking the two supports with innovative competence. Specifically, under AI technological support, AI self-efficacy had a larger mediating association than self-regulated learning; under intelligent environment support, the opposite pattern emerged. This study systematically examined the associations and mediating paths of AI technological support and intelligent environment support with pre-service teachers’ innovative competence. These findings identify correlational patterns consistent with the proposed framework and provide empirical evidence for optimizing AI-empowered teacher innovation cultivation. Keywords: innovative competence, AI technological support, school-based intelligent environment support, AI self-efficacy, self-regulated learning, pre-service teacher

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Age / stage not established. 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 study focused on pre-service teachers at the undergraduate and professional master’s levels in higher education institutions across mainland China. A multi-site, institutionally facilitated convenience sampling approach was adopted. To increase the diversity of the sample across institutional and regional contexts, participants were recruited from higher education institutions located in 12 provinces in eastern, central, and western mainland China. The questionnaire was distributed through established institutional collaboration networks, with the assistance of principals, deans, teaching researchers, and faculty advisors from participating institutions. The questionnaire instructions emphasized principles including voluntariness, anonymity, and truthful completion; obtained informed consent from all participants; and ensured questionnaire validity through three measures: pre-survey control, in-survey monitoring, and post-survey screening. Specifically, first, we controlled the devices used for completing the electronic questionnaire, ensuring each device could only be used for one response. Second, through collaboration supported by the platform of the Center for Teacher Education Research at Beijing Normal University and the Teacher Education and Artificial Intelligence research team, we invited principals, deans, teaching researchers, and faculty advisors from relevant institutions to assist in distributing the electronic questionnaire and ensuring response quality. Third, we manually screened the returned questionnaires, excluding invalid ones with missing items or uniform responses such as fully compliant or very non-compliant. A total of 3214 questionnaires were collected, with 211 invalid ones excluded, leaving 3003 valid questionnaires and a valid response rate of 93.43%.

Limitations and uncertainty · source passage

Although this study drew on a sample of 3003 pre-service teachers from 12 provinces in mainland China and employed quantile regression and structural equation modeling to systematically examine the associations among AI technological support, intelligent environmental support in schools, AI self-efficacy, self-regulated learning, and pre-service teachers’ innovative competence, several limitations should be acknowledged.

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 plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Xu Liu; Meiqi Zhang; Jiaoyang Du. Pre-Service Teachers’ Perceptions of AI Technological Support and School-Based Intelligent Environment Support: Model-Based Indirect Associations with Innovative Competence via AI Self-Efficacy and Self-Regulated Learning. Behavioral Sciences (2026-08-11). https://doi.org/10.3390/bs16081378

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.