Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: From AI literacy to AI-integrated inquiry-based science teaching: the serial mediating roles of AI-TPACK and science teaching self-efficacy among Chinese pre-service science teachers

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): Higher education. Verify the participant population and task before transferring the findings.

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

Introduction Artificial intelligence (AI) is increasingly being integrated into science education, creating new opportunities for visualization, simulation, data interpretation, inquiry-based learning, and scientific explanation. However, it remains unclear how pre-service science teachers translate AI literacy into the professional knowledge and confidence required for AI-supported inquiry-based science teaching. Drawing on AI-TPACK and teacher self-efficacy perspectives, this study examined the serial mediating roles of AI-TPACK and science teaching self-efficacy in the association between AI literacy and Chinese pre-service science teachers’ intention to integrate AI into inquiry-based science teaching. Methods A total of 548 Chinese pre-service science teachers from universities participated in the survey. Data were analyzed using partial least squares structural equation modeling. Results AI literacy was positively associated with AI-TPACK, AI-TPACK was positively associated with science teaching self-efficacy, and science teaching self-efficacy was positively associated with AI integration intention. Further analysis supported a serial indirect pathway linking AI literacy to AI integration intention through AI-TPACK and science teaching self-efficacy. Discussion The findings suggest that AI literacy does not automatically translate into an intention to integrate AI into teaching; rather, this association operates through contextualized technological, pedagogical, and content knowledge and positive capability beliefs. This study extends the application of the AI-TPACK framework to pre-service science teacher education and offers practical implications for systematically developing pre-service science teachers’ AI-related pedagogical competence and integration confidence.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. 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 were 548 Chinese pre-service science teachers enrolled in teacher education programs at normal universities or comprehensive universities in China. Their majors included science education, physics education, chemistry education, biology education, geography education, and related science teacher education fields. Most participants were second-, third-, or fourth-year undergraduates who had generally completed courses in educational technology, science teaching methods, or subject-specific pedagogy and therefore possessed a basic foundation in science teaching and instructional design. This population was selected to ensure that participants could make reasonably informed judgments about AI-supported science teaching and inquiry-based instructional design.

Limitations and uncertainty · source passage

This study makes three theoretical contributions. First, it extends research on AI in teacher education by shifting attention from general AI acceptance to the professional learning mechanisms associated with AI-integrated science teaching. Previous studies have examined pre-service science teachers’ intention to use generative AI in inquiry-based teaching and identified predictors including AI literacy, perceived usefulness, attitudes, subjective norms, and perceived behavioral control (Ramnarain et al., 2025). The present study goes beyond identifying predictors of AI use intention by explaining how AI literacy is associated with AI-TPACK, science teaching self-efficacy, and AI integration intention. It therefore responds to an emerging need in AI teacher education to move from tool adoption toward subject-specific pedagogical integration mechanisms.

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.

  • 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. 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.
  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. 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

Jun Zou; Nan Li; Xiaofei Wang; Juan Du. From AI literacy to AI-integrated inquiry-based science teaching: the serial mediating roles of AI-TPACK and science teaching self-efficacy among Chinese pre-service science teachers. Frontiers in Psychology (2026-09-07). https://doi.org/10.3389/fpsyg.2026.1911909

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.