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Learning with agency: The mechanisms influencing AI teaching readiness among preservice STEM teachers: an empirical analysis based on PLS-SEM and fsQCA

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 Introduction With the rapid integration of artificial intelligence (AI) technologies into basic education, preservice teachers’ AI teaching readiness has become an important factor influencing future AI-supported teaching practices and instructional innovation. Drawing on technology readiness (TR), the technology acceptance model (TAM), and the stimulus–organism–response (SOR) framework, this study developed a mechanism model to explain AI teaching readiness among preservice STEM teachers. Methods This study empirically examined the proposed model using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). In the model, school support was conceptualized as an external stimulus; technology readiness traits, including optimism, innovativeness, discomfort, and insecurity, together with technology-related cognitive appraisals, including perceived usefulness and perceived ease of use, were conceptualized as internal organismic states; and AI teaching readiness was conceptualized as the response. Results The findings showed that perceived usefulness and perceived ease of use were key factors driving preservice STEM teachers’ AI teaching readiness. School support influenced AI teaching readiness through multiple chain-mediating paths involving technology readiness traits and technology-related cognitive appraisals. The PLS-SEM results indicated that the effects of school support on innovativeness and insecurity were not significant. Further, the fsQCA results revealed that, in some high-readiness configurations, school support worked jointly with innovativeness, insecurity, and technology-related cognitive appraisals, while high innovativeness could also compensate, to some extent, for insufficient school support. Discussion The cross-analysis of PLS-SEM and fsQCA revealed both linear pathways and configurational effects in the formation of AI teaching readiness, suggesting that it is not driven by a single factor but by combinations of multiple conditions. This study enriches the application of TR, TAM, and SOR in the context of AI educational technology adoption and provides practical implications for teacher education institutions and school administrators seeking to enhance preservice teachers’ AI teaching readiness. Keywords: AI teaching readiness, fsQCA, PLS-SEM, preservice STEM teachers, technology acceptance model, technology readiness

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

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

To ensure data quality, this study followed the data screening procedure used by Wang C. et al. (2023) and established three questionnaire screening criteria. First, based on the pilot test results, the normal completion time of the questionnaire was approximately 3 to 6 min; therefore, responses completed in less than 2 min were regarded as invalid. Second, a reverse-coded item was included in the questionnaire, and responses from participants who failed to answer it correctly were removed. Finally, questionnaires with identical responses across all items were also excluded. In structural equation modeling research, a ratio of 10:1 between sample size and the number of estimated parameters or observed variables is often regarded as a relatively conservative minimum standard (Bentler and Chou, 1987; Hair et al., 2010). This study included 33 observed variables; therefore, the minimum required sample size was 330. In early December 2025, a total of 633 participants completed the questionnaire. After data cleaning based on the above screening criteria, 550 valid responses were retained, exceeding the minimum sample size requirement and thereby ensuring the robustness of the subsequent statistical analyses (Hair et al., 2016).

Limitations and uncertainty · source passage

Although this study has certain innovations in theoretical integration and methodological application, several limitations remain. First, although the sample size met the basic requirements for PLS-SEM and fsQCA analyses, the sample was drawn only from preservice STEM teachers at two universities in western China. The relatively limited geographical coverage and types of institutions may restrict the generalizability of the findings to other regions, different types of universities, and other teacher groups. Therefore, future research could expand the sample sources by including preservice teachers from eastern, central, and western regions of China, as well as from different types of institutions, such as normal universities and comprehensive universities. Cross-regional, cross-institutional, and cross-disciplinary comparative studies could also be conducted to improve the representativeness and external validity of the findings. Second, this study collected data through self-reported questionnaires, which may be subject to social desirability bias and individual subjective judgment. Future research could combine multiple sources of data, such as interviews, classroom observations, teaching practice performance, or learning analytics data, to enhance the robustness of the findings. Meanwhile, this study mainly used cross-sectional data to examine the relationships among school support, technology readiness, technology acceptance perceptions, and AI teaching readiness. However, preservice teachers’ technology-related psychological traits and cognitive appraisals may dynamically evolve with learning experiences, practice opportunities, and changes in school contexts. Therefore, future studies could adopt longitudinal or experimental designs to further reveal the temporal effects and causal pathways underlying the formation mechanism of AI teaching readiness.

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. 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

Hongbo Zhou; Chen Chen; Yanlong Xie. The mechanisms influencing AI teaching readiness among preservice STEM teachers: an empirical analysis based on PLS-SEM and fsQCA. Frontiers in Psychology (2026-06-26). https://doi.org/10.3389/fpsyg.2026.1868148

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.