INTEGRATING AI AND HUMAN TRANSLATION IN LITERARY TRANSLATION PEDAGOGY: A STRUCTURED PEDAGOGICAL SEQUENCE FOR CRITICAL REFLECTIVE JUDGEMENT
DOI:
https://doi.org/10.35631/IJEPC.1164059Keywords:
Generative AI, Literary Translation Pedagogy, Reflective Judgement, Translator Agency, Arabic Literary TranslationAbstract
Generative artificial intelligence (GenAI) gives student translators additional renderings to consider, each of which still requires human evaluation of meaning, style, and cultural context. This article examines how critical reflective judgement becomes visible in students’ decisions during GenAI-assisted literary translation. The dataset comprises six anonymised student portfolios representing 23 distinct passages and 40 student–passage cases from the Muʿallaqah of Zuhayr, translated into Malay or English. Students produced an initial literal translation, consulted ChatGPT and Gemini using lecturer-provided prompts, prepared a refined version, and justified their decisions in writing. Comparative analysis of successive versions and reflective commentaries traced how students addressed semantic, stylistic, and cultural problems and, where AI-generated alternatives were identifiable, whether they accepted, modified, or rejected them. Critical reflective judgement was clearest when students tested AI suggestions against contextual or literary evidence, assessed their adequacy, and explained why particular renderings were retained, revised, or rejected. The portfolios make the intervening processes of comparison, evaluation, and decision-making more explicit within the staged translation task. They inform a seven-stage pedagogical sequence of translation, AI consultation, comparison, evaluation, decision-making, refinement, and justification. Based on this bounded dataset, the sequence is an empirically informed pedagogical proposition, not a validated model of instructional effectiveness.
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