Teaching AImediated linguistic translation: Language education and the development of lexicosemantic competence
DOI:
https://doi.org/10.7346/sird-1S2025-p1564Keywords:
Language Teaching; AI, Translation; ESP; Language learning.Abstract
In recent years, advances in Artificial Intelligence (AI) applied to Machine Translation (MT) have significantly transformed translation practices, enabling the rapid and increasingly accurate processing of specialized texts. However, the communicative quality and cultural sensitivity that characterise Human Translation (HT) remain essential, especially in technical and scientific domains where terminological precision and conceptual accuracy are keys to disciplinary knowledge transfer. This study presents a comparative analysis between human and machine translations of selected passages from Netter’s Neuroscience Coloring Book (Felten & Maida, 2019), a reference text used in medical English courses. By comparing the professional Italian translation with versions generated by three MT platforms (ChatGPT, DeepL, and Google Translate), the research investigates linguistic, terminological, and pragmatic differences between the two approaches. The methodological design is based on a qualitative, contrastive analysis of selected textual segments, followed by a postediting phase, aiming to identify translation strategies, recurrent errors, and semantic limitations in AIbased systems. From a pedagogical perspective, the study explores the educational potential of comparing HT and MT as a tool to develop linguistic and translation competence in higher education, particularly within English for Specific Purposes (ESP) courses. Findings suggest that the critical and guided use of machine translation can foster metalinguistic awareness,
enhance specialised vocabulary acquisition, and promote intercultural competence among students, positioning
MT as a complementary resource to human translation in advanced language learning contexts.
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Copyright (c) 2025 Francesca Machì
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