AI-POWERED TRANSLATION EDUCATION: A CLIL-BASED HUMAN–AI COLLABORATION CURRICULUM DESIGN - Shi Zhuanzhuan; Wang Hui; Chen Ling (LinguAmerta, 2025)
This research investigates how Artificial Intelligence (AI) tools are being embedded into translation education through a Content and Language Integrated Learning (CLIL) oriented framework. It focuses on two central concerns raised by the teaching community: first, how do translation educators view the integration of AI tools in their teaching, and what difficulties do they encounter when trying to balance these tools with more traditional approaches? Second, what skills do students need in order to collaborate effectively with AI in translation, and how might the curriculum be reshaped to help them build these skills? Through the semi-structured interviews with 9 instructors and focus group discussions with 21 students across three different university types: application-oriented, normal, and academic,the research offers grounded insights into how AI is being experienced in day-to-day translation teaching . The findings reveal that educators view AI as a valuable tool for enhancing translation efficiency but express concerns regarding its impact on critical thinking and cultural sensitivity. Students emphasize the importance of mastering AI tools such as ChatGPT and DeepL, while also critically engaging with AI outputs, particularly in terms of cultural nuances and ethics. The study proposes the integration of AI tools into translation curricula within a CLIL framework, aligning AI-enhanced tasks with the development of content knowledge, language proficiency, as well as cognitive and cultural competencies. These findings point to a pressing need to rethink translation training—not by replacing traditional methods, but by weaving AI use into tasks that still cultivate linguistic nuance, reflective practice, and ethical reasoning.