Post-editing Machine Translation as a Vital Skill in the 21st Century: A Critical Analysis of the Integration of Artificial Intelligence in University-Level Translation Education

Abstract

This study aims to critically examine the integration of post-editing of machine translation (PEMT) in university-level translator training within the context of the growing influence of artificial intelligence. The central research problem concerns the extent to which PEMT is formally incorporated into academic curricula and whether current training programs adequately prepare future translators for professional environments increasingly shaped by AI-driven translation technologies. The study adopts a comparative perspective focusing on three higher education contexts: Algeria, France, and Canada. The research is based on a qualitative and comparative design. The primary methodology consists of a systematic analysis of official curricula, course syllabi, and pedagogical materials related to translator training. This analysis is complemented by a case study approach encompassing both face-to-face and digital learning environments, allowing for an examination of pedagogical practices and technological integration. Analytical criteria derived from translation studies and educational sciences are used to assess the structure and coherence of PEMT instruction. The results reveal significant disparities across the examined contexts. French and Canadian programs demonstrate a relatively structured integration of PEMT, including exposure to professional workflows and translation technologies. In contrast, the Algerian context shows a notable lack of formal pedagogical frameworks and limited institutional recognition of post-editing competences. Moreover, the findings highlight the limited use of standardized machine translation quality assessment metrics, such as Multidimensional Quality Metrics (MQM) and BLEU, within current training programs. The study concludes that translator training curricula require substantial pedagogical updating and recommends the adoption of structured, technology-oriented pedagogical models to better align academic programs with evolving professional demands.