Speaker
Description
As Artificial Intelligence (AI) fundamentally reshapes the professional expectations of the translation industry, higher education must pivot toward a pedagogical model that emphasizes human-AI collaboration. This study, led by Professor Dr. Suyansah Swanto at Universiti Malaysia Sabah, investigates the integration of Machine Translation (MT) and human Post-Editing (PE) within a specialized "Translation In Practice" curriculum. By engaging 60 translation students in a comparative analysis of three distinct workflows-Human-only (HT), AI-only (MT), and Post-Edited (PE)-this research evaluates the intersection of workflow efficiency and linguistic quality. Quantitative findings reveal a significant efficiency gains: the PE workflow was 66% faster than traditional HT methods while achieving comparable quality standards. Qualitatively, the study demonstrates that the PE model fosters "digital fluency" by requiring students to move beyond passive consumption to critical evaluation. Through thematic analysis of reflective journals, students identified consistent AI limitations in cultural nuance, legal terminology, and medical jargon, thereby refining their collaborative problem-solving skills. This presentation argues for a necessary paradigm shift in translation pedagogy. Rather than viewing AI as a replacement for human expertise, the findings support a curriculum that prioritizes AI literacy, Computer-Assisted Translation (CAT) tool proficiency, and specialized Machine Translation Post-Editing (MTPE) strategies. Attendees will explore how this collaborative framework equips future professionals to balance the speed of automation with the critical oversight of human agency.
