Machine-Assisted Translation Education: A Collaborative Framework for AI-Driven Curriculum Reform

Author : Venus Chan

Abstract : This presentation repositions artificial intelligence (AI) and machine-assisted translation (MAT) tools in language and translator education, framing them as drivers of collaborative curriculum reform rather than mere efficiency add-ons. Grounded in the concepts of co-intelligence and "AI+," the study conceptualizes translation training as a joint human–AI enterprise where educators, students, and intelligent systems co-construct professional knowledge and post-editing strategies. While neural machine translation (NMT) and generative AI have rapidly transformed industry practices, current pedagogical approaches often focus narrowly on technical speed, overlooking critical thinking, ethical awareness, and translator agency. Addressing this gap, this paper introduces a pedagogical framework for collaborative curriculum redesign that embeds human–AI co-intelligence across learning outcomes, course structures, and assessment methods. By integrating MAT workflows with emerging technologies, the proposed model prioritizes critical digital literacy, intercultural nuance, ethical reasoning, and professional identity alongside technical proficiency. The presentation also examines key implementation challenges, including mitigating AI overreliance, updating educator competencies, establishing institutional policies, and involving students in curriculum co-creation. Ultimately, it advocates a human-centered approach to machine-assisted translation pedagogy, ensuring future language and translation professionals are prepared for sustainable, ethically grounded human–machine collaboration.

Keywords : AI+, AI literacy, co-intelligence, translation technology, translation and interpreting education.

Conference Name : International Conference on Machine-Assisted Translation and AI (ICMATAI - 26)

Conference Place : Fukuoka, Japan

Conference Date : 22nd Oct 2026