Actionable companion · Motivation & engagement
Learning with agency: From initial trust to critical reconstruction: upper secondary students’ engagement with generative AI in science 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.
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:
Introduction This study examines how upper secondary students engage with generative artificial intelligence (GenAI) in science learning, focusing on how they move from initial trust to critical reconstruction of AI-generated content. Methods Using a constructivist grounded theory design, the study drew on semi-structured interviews with 21 students aged 15-18 from two upper secondary schools in Southwest China. Results The findings show a dynamic process shaped by task demands, prior knowledge, time pressure, and teacher norms. Students often began with efficiency-driven use and provisional trust in fluent, authoritative responses. They then evaluated outputs through internal consistency checking, external corroboration, and growing awareness of GenAI’s limitations. Based on these judgements, students refined prompts, selectively reconstructed content, or abandoned GenAI when outputs were unreliable. Discussion The study argues that GenAI use in science learning is best understood as a form of regulated epistemic engagement.
What kind of evidence is this?
Supporting AI learning science · Qualitative / mixed methods
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.
Participant ages · source passage
Methods Using a constructivist grounded theory design, the study drew on semi-structured interviews with 21 students aged 15-18 from two upper secondary schools in Southwest China.
Study methods · source passage
This study employed a constructivist grounded theory design to investigate how upper secondary students engage with generative artificial intelligence (GenAI) outputs in science learning. A grounded theory approach was appropriate because the study sought to explain an under-theorized process—namely, how students move from initial exposure to AI-generated content toward evaluation, revision, selective integration, or abandonment—rather than to test predefined variables or hypotheses. The constructivist variant was particularly suitable because it treats meanings and categories as co-constructed through the interaction between participants’ accounts and the researchers’ interpretive analysis, while retaining the core grounded theory emphasis on iterative coding, constant comparison, and theory development from data (Charmaz, 2014; Corbin and Strauss, 2015).
Limitations and uncertainty · source passage
A further shift in evaluation occurred when students developed explicit awareness that GenAI could sound convincing while still being wrong. This awareness was usually not theoretical; it emerged from lived experience of failure, such as wrong answers, inconsistent outputs, or poor performance after relying too heavily on AI-generated responses.
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
- 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. 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.
- 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. 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
Chun Zhou. From initial trust to critical reconstruction: upper secondary students’ engagement with generative AI in science learning. Frontiers in Psychology (2026-05-22). https://doi.org/10.3389/fpsyg.2026.1830646
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.