Aug 2026· Frontiers in Education· Vol 11· 0 citations· 35 references
TL;DR
The literature demonstrates a transition from early benchmarking of ChatGPT against human translators and conventional translation technologies toward a more interaction-oriented understanding of human-AI collaboration, suggesting that current translation research is increasingly concerned with whether ChatGPT can translate effectively, but with how it can be integrated responsibly and productively into translation practice, translator education, and human decision-making.
Abstract
The rapid development of large language models, particularly ChatGPT, has generated growing interest in their potential applications in translation studies. Despite the increasing number of publications on this topic, a comprehensive overview of how ChatGPT is being investigated and applied in translation research remains limited.
This study presents a systematic review of research on the use of ChatGPT in translation studies. Using the Scopus database, 1,316 publications published between 2022 and 2025 were initially identified. After the application of explicit inclusion and exclusion criteria, 140 studies were retained for the final synthesis. The selected studies were analyzed to identify publication trends, language pairs, domains of application, and changes in research focus over time.
The findings indicate a marked increase in research output across the examined period, peaking in 2025 with 83 publications. English-Arabic emerged as the most frequently investigated language pair in studies involving source-to-target translation tasks. Thematic analysis identified several major areas of ChatGPT use, including translation pedagogy and training, medical and healthcare translation, literary and creative translation, legal and technical translation, audiovisual translation and subtitling, post-editing practices, and translation quality evaluation. The analysis also revealed a chronological shift in research priorities: studies published in 2023 largely examined user acceptance and preliminary comparisons, research in 2024 increasingly focused on performance testing in specialized domains, and studies published in 2025 placed greater emphasis on effective human-AI interaction and the integration of ChatGPT into translator training.
Overall, the literature demonstrates a transition from early benchmarking of ChatGPT against human translators and conventional translation technologies toward a more interaction-oriented understanding of human-AI collaboration. This shift suggests that current translation research is increasingly concerned not simply with whether ChatGPT can translate effectively, but with how it can be integrated responsibly and productively into translation practice, translator education, and human decision-making.
The present paper investigates the current trends of second language learning and teaching mediated by generative artificial intelligence (GenAI) tools, like ChatGPT and other large language models within the English as a foreign language (EFL) writing context. Scopus, which is a major database, was carefully researched using targeted keywords. All the inclusion and exclusion criteria were implemented consistently, and a total set of 43 articles were finally analyzed to synthesize the present review. The PRISMA method was implemented to ensure transparency and reliability, whereas the bibliometrix R-tool was utilized to generate clear and vivid visual depictions of the bibliometric data. This study adopts a complementary dual-method design, combining a scoping review approach with a bibliometric analysis to investigate the use of GenAI in EFL writing instruction. The scoping review serves as the primary analytical component of the study, identifying major pedagogical themes, applications, and challenges related to artificial intelligence-supported writing enhancement. The bibliometric analysis is used to contextualize the structure and evolution of the field by mapping its intellectual structure, publication evolution, influential publications, productive authors, institutional affiliations and keyword trends. The synthesis of the reviewed studies indicates that AI-assisted feedback is frequently associated with improvements in grammar, vocabulary, coherence, and learner engagement. Several studies also highlight challenges, including concerns about academic integrity, students’ overreliance on AI, and the necessity for teacher guidance. By systematically synthesizing these results, this review offers a clear picture of current practices, emerging trends, and research gaps, providing insights for researchers, educators and policymakers in the EFL domain. This study has certain limitations such as filtering constraints and strict time limits, but there are some useful guidelines that researchers can utilize in their future research.
Whether contemporary LLMs can reproduce the research outcomes of a fully documented human study: a 1991 article that identified dermatophytosis (ringworm) in historical fine art was evaluated.
The release of ChatGPT in November 2022 transformed second language (L2) writing instruction and led to rapid growth in research on large language model (LLM)-generated feedback; however, no synthesis has mapped this literature in terms of feedback quality, learner uptake, and pedagogical integration. This scoping review examines 185 empirical studies published between November 2022 and March 2026 that were identified through a Scopus search (n = 283 screened) and analysed using a systematic keyword-based charting framework applied to full abstracts, with full-text analysis of 35 studies. The review identifies four major patterns: (1) comparative AI–human feedback research dominates the literature (27.6%); (2) content-level feedback remains underexplored (13.5% of studies); (3) learner uptake is rarely measured as a primary outcome; and (4) LLM feedback is broadly comparable to teacher feedback for surface-level errors but weaker for content and argumentation, while learner perceptions often exceed demonstrated performance outcomes. Hybrid AI–teacher models show promising but underexamined potential, accounting for only 12.4% of the literature. The field shows a focus on perceptions rather than learning outcomes, an apparent tendency toward positive-results reporting, and no clear teaching models. This study proposes a typology of LLM feedback functions and outlines a research agenda focused on uptake, longitudinal outcomes, and hybrid AI–teacher integration.
Laurence Craven, Daniel R. Fredrick· Education sciences· 0 citations
This exploratory study presents the initial results of a comparative analysis designed to evaluate the performance of human coding and GenAI (ChatGPT 5.1) coding in qualitative content analysis. The analysis focuses on six selected articles from Italian media outlets (La Repubblica, Il Corriere della Sera, Il Giornale) covering the Russia–Ukraine conflict from 2022 onward. These six articles were selected from a broader corpus of 180 publications, which served as the basis for the sampling procedure. The findings reveal both opportunities and limitations in the use of AI for qualitative research. On the one hand, ChatGPT provided additional capacity for identifying linguistic units that had been overlooked by the human coder. On the other hand, ChatGPT showed limitations related to its constrained linguistic capabilities, which prevented it from fully capturing complex linguistic figures associated with judgment values. This research offers methodological insights for communication scholars regarding the responsible and effective integration of GenAI into qualitative media studies. Future research will extend the qualitative analysis to the broader corpus, with attention to framing strategies, manipulative practices, and objectivity markers. This continued comparative approach will allow for a more detailed mapping of similarities and differences in coding tendencies between human coding and AI, as well as for the detection of ideological patterns in Italian media coverage of the Russia-Ukraine conflict.
Anastasiia Iufereva· Journal of Media Research· 0 citations
The scientific literature surrounding the pioneering tool, Chat GPT, is vast and rapidly growing; however, the scope of research areas remains limited. The aim of the study is to conduct a bibliometric analysis to identify publications related to Chat GPT. Using the search terms ‘Chat GPT’ and ‘Chat GPT’, all publications related to Chat GPT from the Web of Science database were extracted. A descriptive analysis of 4177 publications from January 2021 to August 2024 was conducted by focusing on publication trends, influential authors, and research themes. A total of 4177 Chat GPT-related publications were published in 1697 different sources. The analysis shows that the majority of research concentrated in the fields of health sciences and applied sciences, with significant contributions from the United States, China, and Singapore. Notably, the most productive journal is the
CUREUS Journal of Medical Science
, while collaboration between authors is mainly found in developed countries. The study highlights key trends in Chat GPT–related publications, with the literature most frequently focusing on the fields of health sciences and applied sciences, while social sciences are less represented. This indicates a need for broader exploration of Chat GPT’s impact across various disciplines to fully understand its potential and implications.
M. A. Arık, Ayşen Yalman, Tuba Livberber et al.· Journal of information scien...· 0 citations
An updated overview of EFL writing pedagogy is provided, which will be useful for researchers, teachers, educators, and curriculum designers working on development of instructions for modern educational context, keeping in mind the pedagogical rigor.
Esraa Abd Elnaser Mohamed Hassan, R. Mia, Md Shafiqul Islam· International journal of res...· 0 citations
