Comparing Generative AI and Advanced Learners in Translation Tasks Using the House Model: Similarities and Pedagogical Implications
Year: 2025
Type: article
Abstract: This research examines the performance of Generative AI models (Gen AIs) compared to advanced language learners of Chinese in prompted translation tasks.We utilize discourse analysis (Bender & Lascarides, 2019;Cruse, 2011;Goddard, 1998;Kroeger, 2019), especially social-cultural analysis, through the functionalist model of translation quality assessment framework proposed by House (1997House ( , 2014)).This model assesses the functional equivalence of the translations through linguistictextual analysis across four dimensions, i.e., 1) Genre and three classic Hallidayan register categories, 2) Field, 3) Mode, and 4) Tenor.It is often observed that Gen AIs excel at generating error-free sentences due to their extensive linguistic databases, while advanced learners produce few errors by using alternative expressions for gaps of their linguistic knowledge, a communicative strategy, i.e., circumlocution or avoidance (Váradi, 1980;Dörnyei & Scott, 1997;Kasper & Kellerman, 1997).Therefore, the output of writing tasks presents difficulties to close comparison due to its variability.Unlike writing, translation requires precise message conveyance without paraphrasing.In our study, three advanced Chinese learners and five Gen AI models completed translation of eight texts (each with a word count of 400-500) from English to Chinese.When evaluated with this model, Gen AIs produce awkward or inappropriate translations, failing to capture nuanced tones, styles, and contextual suitability.Similarly, advanced learners occasionally choose expressions that, while seemingly correct, are contextually inappropriate, highlighting the need for refinement in their language proficiency.To leverage these similarities in functional mismatches, we propose a collaborative revision process (Min, 2006;Hu & Lam, 2010;Henshaw, 2023) in advanced language teaching.Learners compare their translations with those generated by Gen AIs and revise them based on the teachers' feedback on discourse and functional issues to improve their linguistic competence.This research presents instructional design ideas, classroom activities, and student assignments for implementing this collaborative revision process effectively.
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4411454942