Policy On The Use Of Machine Translation (mt): A Good Model For Wider Policies On Generative Ai (genai)?

Since the advent of ChatGPT and other automatic text generators, educators from many disciplines, including language learning and teaching, have published numerous articles exploring this technology’s “pitfalls and potentials” (Barrot, 2023) and offering recommendations based on their own practice to teachers, users, and institutional decision-makers. But it is early days yet, and, while recognising the need to offer guidelines, there is not enough scientific data to create evidence-based guidelines. Having been working on machine translation (MT) literacy (Bowker & Buitrago Ciro, 2019; Cotelli Kureth & Summers, 2023) for several years, we have developed guidelines for the use of machine translation (MT) tools in higher education, which have been implemented in a Swiss university. Given that MT tools share technical features with generative AI (GenAI) tools like ChatGPT, we believe that applied knowledge of the former could facilitate understanding of the latter. This article will draw on both our own experience and a thorough literature review of recommendations for the use of GenAI for higher education institutions (HEI) to map what guidelines on the use of GenAI should include and how they should be presented to teachers and users.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-05-01
Primary Topic
Artificial Intelligence in Healthcare and Education
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article
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article

Policy On The Use Of Machine Translation (mt): A Good Model For Wider Policies On Generative Ai (genai)?

SHILAP Revista de lepidopterología
Artificial Intelligence in Healthcare and Education
article

Policy On The Use Of Machine Translation (mt): A Good Model For Wider Policies On Generative Ai (genai)?

article en

Abstract

Since the advent of ChatGPT and other automatic text generators, educators from many disciplines, including language learning and teaching, have published numerous articles exploring this technology’s “pitfalls and potentials” (Barrot, 2023) and offering recommendations based on their own practice to teachers, users, and institutional decision-makers. But it is early days yet, and, while recognising the need to offer guidelines, there is not enough scientific data to create evidence-based guidelines. Having been working on machine translation (MT) literacy (Bowker & Buitrago Ciro, 2019; Cotelli Kureth & Summers, 2023) for several years, we have developed guidelines for the use of machine translation (MT) tools in higher education, which have been implemented in a Swiss university. Given that MT tools share technical features with generative AI (GenAI) tools like ChatGPT, we believe that applied knowledge of the former could facilitate understanding of the latter. This article will draw on both our own experience and a thorough literature review of recommendations for the use of GenAI for higher education institutions (HEI) to map what guidelines on the use of GenAI should include and how they should be presented to teachers and users.

SHILAP Revista de lepidopterología
University of Neuchâtel (CH), ZHAW Zurich University of Applied Sciences (CH)
Quality Education
Openalex Percentile: Top 53%
Artificial Intelligence in Healthcare and Education
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