AI Era Classroom Beyond Post-Editing: Custom Gpts in Translator Training - Karaban , Viacheslav; Karaban , Anna (The Journal of V.N. Karazin Kharkiv National University. Series: Foreign Philology. Methods of Foreign Language Teaching, 2025)
The article addresses the current pedagogical dissonance in translator training, where Large Language Models (LLMs) are either banned or students are confined to the remedial task of post-editing of LLM output, neglecting the critical up- and downstream decisions. We propose a structured integration of custom GPTs for translation teaching into the curriculum, reframing AI from a generic tool into a suite of specialized assistants for the pre-production, production, and post-production phases of the translation workflow. This approach makes the translator’s decision-making process transparent, teachable, and assessable, shifting the focus to strategic thinking. We situate this design in current work on LLM-assisted translation, post-editing, automated evaluation, and AI literacy, and recommend human oversight to limit hallucinations and biases. Methodologically, the paper offers a narrative synthesis of pedagogical, professional, and ethical arguments for the adoption of the custom GPTs and formalizes their role/function action plan. The paper presents a framework and a small proof-of-concept custom GPT suite spanning sense resolution, synonym precision, challenge identification, term extraction, explainable translation, and quality assurance. Early use indicates the following benefits: metacognitive lift via explicit alternatives, rationales, and confidence; efficiency without opacity, as assistants recommend while students decide. By embedding these role-based GPTs, educators can foster essential competencies like AI literacy and prompt engineering, while students gain agency and a deeper understanding of the translation process. The teacher-machine-student triad recenters agency and accountability in translator training. This approach promotes the development of crucial, future-proof skills and positions technology as a tool that augments and enhances human capabilities within a human-centered AI (HCAI) paradigm and provides an actionable path for educators to move beyond the post-editing bottleneck, transforming AI from a forbidden shortcut into a structured apprenticeship in translator thinking for an AI-integrated industry.