Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: The effects of AI-collaborative translation workshop on translation competence and learner engagement: an empirical study in a Chinese university translation course

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:

As artificial intelligence (AI) is integrated into translation pedagogy, it brings new possibilities for competence development. However, concerns also emerge regarding learner engagement and maintenance of human agency. This study investigates the effects of an AI-Collaborative Translation Workshop on translation major students’ learner engagement and translation competence with a mixed-methods approach. 69 undergraduate translation-major students participated in a 16-week quasi-experimental design. The experimental group (n = 35) attended a structured AI-collaborative workflow, while the control group (n = 34) took a conventional translation course concurrently. Through pre- and post-intervention scales and translation competence tests, quantitative data were gathered, while classroom observations and semi-structured interviews generated qualitative data. According to the ANCOVA results, significantly greater improvements in overall translation performance and engagement were observed in the experimental group. Specifically, the intervention yielded moderate-to-large effects on textual faithfulness and linguistic appropriateness, but the effectiveness of cultural transmission showed no pronounced improvement. In terms of learner engagement, the intervention exerted moderate positive effects on overall engagement, with considerable improvements in behavioral and emotional engagement. No significant change was found in cognitive engagement. Qualitative data corroborated these findings. Students in the experimental group demonstrated more proactive behaviors and positive emotions. Overall, despite strengthening behavioral and emotional participation as well as certain aspects of translation competence, AI-human collaboration still had limited ability to cultivate deep cognitive engagement and culturally sensitive translation skills. Therefore, pedagogical designs should position AI as a resource for critical reflection rather than a substitute for translator decision-making, thereby preserving learner agency and promoting the development of higher-order thinking.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

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 involved in this study were 69 third-year translation majors at a university in southeastern coastal China, comprising 15 male students (21.74%) and 54 female students (78.26%). All were taking the same Chinese-English translation course, and had completed the foundational translation courses required by the curriculum. Prior to the intervention, all participants followed an identical syllabus to ensure comparable academic backgrounds. They were assigned to either the experimental group (n = 35) or the control group (n = 34) based on their existing class rosters via a non-random assignment. Participants demonstrated comparable English proficiency levels based on their National College Entrance Examination (Gaokao) scores (Mexperimental = 108.18, SD = 11.93; Mcontrol = 107.31, SD = 15.12), with an independent samples t-test revealing no significant difference between the groups (t = 0.262, p = 0.794). No participants withdrew from the study throughout the 16-week intervention.

Limitations and uncertainty · source passage

Several limitations of this study should be acknowledged. Firstly, this study employed a quasi-experimental design involving two intact classes, and participants were not randomly assigned to the experimental and control groups. Due to possible pre-existing differences between groups, the observed outcomes may be affected. Secondly, given that this intervention combined the use of AI tools with collaborative learning activities, the independent effects of each could not be fully separated. The combined influence of both instructional elements may have triggered the observed improvements. Thirdly, the sample size was relatively small, restricting the findings’ generalizability. Lastly, the intervention lasted for only a short period of time, so that the delayed effects of the AI-collaborative translation workshop on translation competence and learner engagement could not be examined.

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

Weihe Xue. The effects of AI-collaborative translation workshop on translation competence and learner engagement: an empirical study in a Chinese university translation course. Frontiers in Psychology (2026-08-20). https://doi.org/10.3389/fpsyg.2026.1918404

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.