Machine Translation (MT), especially the one powered by Artificial Intelligence, has introduced new dynamics in the translation industry as well as in translation training. The integration of MT into Translation Studies is reshaping the industry and academic training. MT is increasingly central in translator education, highlighting the importance of technical and linguistic skills. However, post-editing, crucial for refining MT output, remains underemphasized in standard translation curricula, being confined mainly to specialized courses. This gap calls for comprehensive training that combines theoretical, practical, and critical perspectives. This study, involving Translation and Interpreting students, examined their perceptions of MT and post-editing through pre- and post-activity questionnaires. Findings revealed that while students frequently use MT and view it as beneficial for handling large texts, they initially lacked confidence in its efficacy and quality. Post-activity results indicated a more positive view of MT’s role but also highlighted increased dependency and reduced self-assurance in their translation skills. These insights underscore the necessity of balanced, critical MT training to foster informed and effective translators.