Generative AI in Translation: Trust, Transformation, and Pedagogical Integration in the Post-LLM Era - Yaqin, Lalu Nurul; Ismail, Isma Noornisa (International Journal of Linguistics and Indigenous Culture, 2026)
The emergence of large language models (LLMs) like Catgut has sparked both excitement and anxiety in the translation field. This literature review critically examines how generative AI is reshaping professional translation and translator education in the post-LLM era. Early studies showed that ChatGPT could produce translations between major languages with quality on par with neural machine translation, fueling hopes for productivity gains. However, trust in generative AI among translators remains limited. Surveys indicate that practitioners adopt tools like ChatGPT cautiously, primarily for ancillary tasks such as summarisation or drafting ideas. Key concerns, including data privacy, confidentiality, output accuracy, and the threat of job displacement, undermine translators’ trust in AI systems. We situate these concerns within technology acceptance models (TAM2) and AI trust frameworks, highlighting how perceived usefulness, reliability, and transparency shape adoption. At the same time, generative AI is transforming translation workflows: translators’ roles are evolving toward post-editing and quality control, raising questions of professional identity and autonomy. In translator education, recent experiments integrating ChatGPT show improved student performance and confidence, provided that pedagogical approaches emphasise critical AI literacy and post-editing skills. This review synthesises theoretical and empirical insights from 2023 to 2025 to present a balanced perspective on generative AI in translation. We argue that a human-centric integration of GenAI, one that builds translator trust, addresses ethical/legal risks, and nurtures new competencies, will be essential for harnessing these technologies’ benefits without undermining professional standards.