Agentic education · Learning science

Actionable companion · Learning design & outcomes

From AI evidence to responsible practice: Modeling foreign language majors’ adoption intention of Chinese GenAI tools: an extended TAM with perceived teacher attitude and peer influence

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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 participants, intervention and comparator before applying this study in a new setting.

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

This study explores the key determinants shaping foreign language majors’ adoption intention toward Chinese generative artificial intelligence (GenAI) tools. Using an extended Technology Acceptance Model (TAM), it examines the underexplored roles of two critical social factors: perceived teacher attitude (PTA) and peer influence (PI). A quantitative methodology was adopted, utilizing an online questionnaire to collect data from 423 foreign language majors across multiple universities in China. The proposed theoretical model and hypothesized relationships were rigorously analyzed through structural equation modeling (SEM). The results identified PI and attitude as direct positive predictors of adoption intention. Notably, restrictive PI was not significantly associated with adoption intention, whereas supportive PTA showed a significant positive association. This research extends the TAM and implied that simple prohibitions or passive advocacy are insufficient to promote the effective application of AI. Educational stakeholders should take practical measures to go with the future trends in Human-Machine Collaboration.

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

In this study, the 423 participants were foreign language majors from 6 undergraduate universities, located in Guangxi Zhuang Autonomous Region and Hunan Province, two bordering provincial level administrative regions in southern China.

Limitations and uncertainty · source passage

Several limitations should be acknowledged. First, the cross-sectional design prevents causal inference. Future research should employ longitudinal designs to examine how students’ perceptions of teacher attitudes and peer influence evolve over time and whether they predict actual adoption behavior.

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. 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

Mei Huang; Ling Huang. Modeling foreign language majors’ adoption intention of Chinese GenAI tools: an extended TAM with perceived teacher attitude and peer influence. Frontiers in Psychology (2026-06-30). https://doi.org/10.3389/fpsyg.2026.1850558

Companion prepared 2026-11-01. 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.