This master’s thesis explores the integration of machine translation (MT) and post-editing (PE) in English-to-Arabic translator training. It examines two main questions: how post-editing affects translation quality in a learning context, and how students perceive its inclusion in coursework. An experimental study was conducted with four Arabic-speaking translation students, who post-edited legal, financial, and journalistic texts generated by DeepL using the MateCat platform. Feedback was collected through pre- and post-task questionnaires. Findings showed that participants responded positively to using MT and PE in training. The quality and type of MT output varied across text types, influencing the nature of edits. Despite stylistic or accuracy issues in the raw output, students improved translations significantly through post-editing. These results suggest that post-editing is a valuable skill for translators and should be integrated into translator education. Further research with a larger sample and broader text selection is recommended to confirm these findings.