The translation industry is undergoing a major technological shift, marked by a growing demand for post-editing (PE) and the emergence of machine translation (MT) tools as an essential part of the translators’ workflow. This study aims to explores the effects of neural machine translation (NMT) on the quality of the target language after PE (English-French) in a pedagogical context. Three linguistic metrics –lexical richness, average sentence length and syntactic equivalence–were used to analyse our two corpora of student’s productions. The results show that, although lexical density and average sentence length do not differ significantly between PE and human translation (HT), post-edited texts –especially when using DEEPL–, were found to be less lexically diverse and syntactically closer to the source language than HT, revealing the evidence of typical features in MTPE (post-editese).