ArticlePloS one2025
Integrating human-AI collaboration into translation education: A comprehensive protocol for assessment, diagnosis, and strategy development.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of artificial intelligence (AI), translation education faces pressing challenges to integrate human-machine collaboration into talent cultivation. This study protocol outlines a two-year mixed-methods project that focuses on developing, validating, and applying systematic tools for assessing human-machine collaborative competence among translation students and teaching competence among translation instructors. In the first stage, measurement instruments will be constructed and validated through thematic analysis, exploratory and confirmatory factor analysis, based on interviews, pilot testing, and large-scale surveys. The second stage will employ grounded theory, structural equation modeling, and regression analysis to identify the factors influencing students' and teachers' competence development. The third stage will investigate industry needs through semi-structured interviews with translation service professionals and descriptive statistical analysis across multiple domains. The fourth stage will assess classroom effectiveness via multimodal analysis of teaching and learning processes across five types of universities. Finally, a comprehensive training strategy will be designed, refined through action research pilot implementations, and validated by Delphi consultation with translation educators, industry specialists, and policymakers. By integrating empirical rigor with iterative validation, this study advances theoretical modeling of human-AI collaboration, establishes robust assessment tools for students and teachers, and delivers actionable training strategies that align translation education with evolving professional and industry needs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.