Localization at the Dawn of Generative AI edited by Mária Koscelníková and Kristijan Nikolić.

09.01.2026

Since the advent of machine translation (MT) and its profound impact on translation practice, artificial intelligence (AI) stepped into the game with new levels that reshaped the ways translators engage with language, meaning and creativity. From rule-based and statistical machine translators (Poibeau, 2017; Naveen & Trojovský, 2024) to neural machine translators (NMTs) and, most recently, generative AI-driven chatbots (GenAI), translation and localization workflows have become increasingly automated, accelerated and data-driven. Today, translations can be generated within seconds, while generative chatbots and other systems frequently influence decision-making process through predictive suggestions, adaptive prompts and multimodal outputs.

Scholars like Yu & Lu (2021), Chen (2024), Penet et al. (forthcoming) already reflected the impact of generative AI on translator education and competence development. In the domain of game localization, Al-Batineh, M. & Alawneh, R (2022) investigated the impact of AI-assisted translation with a focus on Arabic, Cairns, R. (2023) and Hanussek & Kot (2024) drawn attention to the transformative potential of GenAI in game localization, while with the greater focus on general localization, “recent trends highlight the growing trust in AI translation, as well as the continued need for human expertise in refining AI-generated translations to capture cultural nuance and industry-specific terminology” as Tabrizi (2025) points out.

It is widely acknowledged that many sectors are currently undergoing substantial transformations in their workflows due to the rapid advancement of artificial intelligence. Localization, however, remains a domain in which creativity and contextual decision-making are essential for adapting products to the expectations, conventions, and constraints of specific locales. As the profession evolves, translators and localization professionals worldwide are confronted with new working conditions and an increasing demand for redefined and expanded competences. This special issue aims to investigate how generative artificial intelligence (GenAI) challenges, reshapes, and coexists with human creativity and the translation process within localization practices.

The journal welcomes original contributions addressing, including but not limited to, the following topics:

  • Integration of GenAI and NMT into localization training
  • Emerging localization competences in the age of GenAI (e.g. post-editing, prompt engineering, quality evaluation)
  • GenAI-assisted localization in less widely spoken and low-resource languages
  • Ethical, legal, and responsibility-related issues of GenAI and NMT in localization
  • Accessibility and inclusivity in GenAI-driven localization
  • GenAI, creativity, and authorship in localization processes
  • Theoretical perspectives on localization in the context of generative AI
  • GenAI applications in software and user-interface localization
  • Web localization and adaptive content generation using GenAI
  • Game localization and procedural or AI-generated narrative content
  • Localization of mobile applications and multimodal GenAI workflows

Deadline for submission: 31st July 2026

Please, if you are considering publishing with us, send us your submissions to: editorial@l10njournal, Re: Special issue 2026

 

References

Al-Batineh, M. & Alawneh, R. (2022) Current trends in localizing video games into Arabic: localization levels and gamers’ preferences. Perspectives, 30:2, (pp. 323-342). https://doi.org/10.1080/0907676X.2021.1926520  

Cairns, R. (2023). ‘Video games are in for quite a trip’: How generative AI could radically reshape gaming. https://edition.cnn.com/world/generative-ai-video-games-spc-intl- hnk/index.html

Chen, X. (2024). Research on the application of artificial intelligence in translation courses. International Journal of Education and Humanities, 12(1), 41-44. https://doi.org/10.54097/3c1b8w36

Hannusek, B., Kot, Y. (2024). Generative AI in Video Game Production: More Content does not (necessarily) mean more Money. Money | Games | Economies, University of Krems Press, 191-209 DOI: https://doi.org/10.48341/pwsk-m637

Penet, JC, Moorkens, Joss & Yamada, Masaru (eds.). Forthcoming. Teaching translation in the age of generative AI: New paradigm, new learning?. (Translation and Multilingual Natural Language Processing 25). Berlin: Language Science Press. DOI: 10.5281/zenodo.17580856

Tabrizi, S. A. (2025). Engineering-Centered Quality Assurance in Software Localization: Aligning GUI and MT Evaluation with the McCall Software Quality Model. Emerging Technologies and Engineering Journal. 2025, 2(2), 69-80. https://doi.org/10.53898/etej2025225

Sandrini, P. (2025). Creativity as a sustainable future for translators? Marco Agnetta, Katharina Walter (Hg.): Künstliche Intelligenz in der Sprachmittlung und im Fremdsprachenerwerb: Ausbildung und Arbeitsmarkt im Wandel/Artificial Intelligence in translation, interpreting, and foreign language learning: Educational and vocational changes. Themenheft Trans-Kom, 18(1), 83–94.

Yu, S., Lu, Y. (2021). An Introduction to Artificial Intelligence in Education. Springer. https://doi.org/10.1007/978-981-16-2770-5