Actionable companion · Accessibility & inclusion

Learning with agency: Human or machine? Do source beliefs shape cognitive bias in post-editing?

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:

As artificial intelligence (AI) translation becomes increasingly integrated into professional and educational contexts, perceptions of “machine vs. human” translation may shape translators’ cognitive and evaluative and post-editing behaviors. This study investigates the role of cognitive bias in post-editing, examining how translators’ beliefs about translation sources—human or AI—trigger systematic deviations in evaluation and revision behavior. Sixty master students in Translation and Interpreting participated in a between-subjects experiment: the experimental group received mislabeled texts (human translations labeled as AI, AI translations labeled as human), while the control group was informed of the true sources. Participants evaluated two English-to-Chinese translations on fidelity, fluency, and completeness (5-point Likert scales) and performed post-editing, with modifications recorded for type and rationale. Quantitative analyses (revision counts, quality ratings) and qualitative reflections were examined to explore how cognitive preconceptions modulate linguistic judgment and editing behavior. Findings indicate that perceived AI labels elicited lower trust, increased post-editing intensity, heightened error sensitivity, and more conservative quality ratings, even when translation quality was equivalent. This reveals a subtle form of language-related cognitive bias, in which perceived source identity, rather than objective quality, drives evaluation and modification decisions. By bridging translation studies and cognitive psychology, the study provides empirical insight into human–AI interaction and offers pedagogical implications for fostering critical post-editing literacy and inclusive attitudes toward AI-mediated communication.

What kind of evidence is this?

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

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

A total of 60 MA students enrolled in a Master of Translation and Interpreting (MTI) program participated in this study on a voluntary basis. Participants were randomly assigned to either an experimental group or a control group, with 30 participants in each group. All participants had received formal training in translation and possessed prior experience with translation revision tasks as part of their MTI coursework. No participant reported prior professional experience in developing or training AI translation systems.

Limitations and uncertainty · source passage

Regarding the study’s limitations, the findings are based on a focused sample from a single MTI program and a limited set of translation stimuli. While providing a controlled environment for observing labeling effects, this specific scope may constrain their generalizability across broader professional contexts, diverse language pairs, and varied text types. To build on this foundation, future research should employ more diverse participant profiles and varied materials to test the robustness of the dimensional susceptibility hierarchy documented here, examine how labeling effects interact with professional experience and occupational identity, and investigate whether workflow-level interventions can effectively disrupt the schema-activation pathway through which source attribution currently operates.

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.

Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

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. 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.
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. 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

Jing Cao. Human or machine? Do source beliefs shape cognitive bias in post-editing?. Frontiers in Psychology (2026-04-22). https://doi.org/10.3389/fpsyg.2026.1819659

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.