Jul 2026· English Language Teaching· 0 citations· 30 references
TL;DR
Light is shed on the process-oriented nature of translation strategies and empirical evidence for integrating technological tools into translation pedagogy is offered.
Abstract
As AI has been increasingly applied in translation, Large Language Models (LLMs) are becoming important tools in translation practice. Both human translators and AI models employ strategies in the translation process, yet comparative studies remain limited. Using a self-developed Translation Strategy Evaluation Scale (TSES), this study drew on scientific, political-economic, and literary texts to compare human and AI translation strategies. Think-Aloud Protocols (TAPs) and AI reasoning outputs were used to examine differences in strategy types and decision-making between two human translators and two LLMs (GPT-5.3 and DeepSeek-V3.2); the Many-Facet Rasch Model (MFRM) was then applied to evaluate strategy use across four dimensions: necessity, compatibility, effectiveness, and consistency. Results show that while both shared 7 basic categories, they diverged in 4 respects. AI used abstraction and nominalization for stylistic formality; human translators performed grammatical monitoring, redundancy reduction, and discourse reorganization. In decision-making, AI followed systematic planning and rule-driven execution, while human translators relied on experiential judgment and reader awareness with ongoing self-monitoring. ChatGPT demonstrated the highest quality of strategy use across all text types. The study sheds light on the process-oriented nature of translation strategies and offers empirical evidence for integrating technological tools into translation pedagogy.
Backround - The rapid advancement of Artificial Intelligence (AI) in translation studies has transformed how literary texts are processed, shifting from literal word-for-word transfer to more contextually nuanced approaches. Literary fables demand particular sensitivity to personification, onomatopoeia, and moral messaging.
Urgency of Research - Despite the proliferation of AI translation tools, previous studies have predominantly focused on macro-level quality evaluation (e.g., fluency and accuracy) rather than examining the micro-linguistic strategies AI employs. There remains a significant gap in understanding how different AI models identify and apply established translation techniques within Molina and Albir's comprehensive framework of 18 translation techniques.
Research Objectives - This study aims to evaluate and compare the performance of four AI tools—ChatGPT, Gemini, Claude, and DeepL—in identifying and applying translation techniques in the literary fable "The Clever Rabbit," specifically examining how each model utilizes Molina and Albir's 18 translation techniques to achieve dynamic equivalence.
Research Method - Adopting a qualitative descriptive approach, this study employs purposive sampling to select translation units demonstrating specific techniques. Data were collected through comparative textual analysis of one English source text and four Indonesian target texts, validated through theoretical triangulation and source triangulation.
Research Findings - The findings reveal a clear strategic polarization: generative AI models (ChatGPT, Gemini, Claude) demonstrate dominance in complex transformation techniques such as Modulation (20-23%), Equivalence (12-13%), and Explicitation, reflecting deeper contextual understanding. In contrast, DeepL shows extreme reliance on Literal Translation (>65%) with minimal cultural or stylistic adaptation. ChatGPT excels in local adaptation through generalization and particularization; Gemini stands out in narrative vitality through expressive lexical variation; and Claude offers structural efficiency through precise grammatical reduction.
Research Conclusion & Novelty - This study concludes that while all AI tools can transfer denotative meaning, generative models (LLMs) are superior in applying high-level translation techniques necessary for maintaining emotional nuance, discourse cohesion, and literary appeal. The novelty lies in its micro-linguistic analysis using Molina and Albir's comprehensive taxonomy across four distinct AI platforms, providing unprecedented insight into the "black box" of AI translation strategies. The findings offer practical guidance for educators, researchers, and translators in selecting appropriate AI tools, emphasizing that critical human post-editing remains indispensable for achieving true literary equivalence.
The findings show that AI-generated texts exhibit greater lexical diversity and syntactic complexity; however, they often exhibit structural uniformity, overuse of cohesive devices, and limited pragmatic depth, and should not replace professionally designed educational materials.
V. Smaglii, T. Korolova, Svitlana Yukhymets et al.· Arab World English Journal· 0 citations
This study investigates grammatical trends in texts generated by artificial intelligence and human learners. The study puts to the test a fundamental principle of usage-based grammar: language is learned through repeated exposure to patterns. A direct comparison is conducted between AI-generated writings and language learners' essays. Quantitative approaches count words, sentences, and grammatical errors. Qualitative analysis detects trends in sentence structure and specific qualities such as past tense. Finding out if AI models adhere to usage-based grammar rules is the aim. Comparing the two groups' mistake types is another objective. The results show that whereas human writing varies, AI output is very constant. Almost no grammatical errors were found in AI articles, according to the study. Expected errors in human texts include omissions and overgeneralizations. The findings also demonstrate that AI makes greater use of components like the past tense and plurals. These studies demonstrate that the outcomes of usage-based learning are operationally replicated by AI. The results of training the model on massive amounts of data are consistent and precise. The ongoing process of language acquisition is reflected in human output. The study comes to the conclusion that AI is a powerful instrument for confirming frequency-based linguistic theory.but does not model the human cognitive journey. Future research should investigate different AI models and learner proficiency levels
Assis. lect. Batool Abdul-Mohsin Miri· Journal of College of Educat...· 0 citations
This study examines the performance of various machine translation (MT) systems and a generative AI (GenAI)
translation in the context of game text translation using data from the popular racing game
Need for Speed:
Unbound
(
Criterion Games 2022
). Focusing on factors such as ambiguity,
contextual disambiguation, stylistic variations, domain specific knowledge, and technical entities (e.g. placeholders), the
analysis compares human translations with outputs from Google Translate, DeepL, Papago, and ChatGPT. Three representative examples
from a game script are examined, revealing that while MT and GenAI systems can preserve lexical content, they often fail to
capture critical nuances and contextual meanings that are crucial for interactive gaming environments. The findings highlight the
need for integrating additional contextual information and domain-specific post-editing to improve MT and GenAI translation
quality for translated game texts, contributing to the broader discussion on enhancing interactive media translation.
J. Y. Kim, Eunjoo Kwak, Dong-mie Kim· Digital Translation· 0 citations
It is noted that current use cases highly depend on the severity and context of the situation in which translations are being produced, but that the future of language models supporting accessible and quality translations is optimistic.
The study finds that each prompt orientation produces distinct and observable shifts in diction, imagery construction and formal expression, and re-conceptualizes prompts not only as technical input instructions, but as purposive regulators of translation, offering a translation-theoretic framework for analysing LLM behaviour and practical guidance for designing Skopos-informed prompts for AI-assisted literary translation.
Haijin Li· Journal of Translation and L...· 0 citations
