With the hype surrounding neural machine translation (MT), it is easy to forget that MT technologies, although increasingly integrated into the professional translator’s toolkit today, were not initially developed with professional translators in mind. While the productivity gains brought about by MT can help translators meet ever-tighter deadlines and respond to pressing demands for publishing content simultaneously in different languages, these tools also impose a workflow that tends to reduce the human translator’s role to simply correcting mistakes made by the machine, in a one-way process with no real interaction. Thus, although more cost-effective, post-editing of MT output also appears a less creative and enjoyable a task than translation. Adaptive MT, on the other hand, has been advertised as a way to recenter the translation process on the human and foster more genuine interaction with the machine. Said to have been developed for professional translation workflows, the technology enables a dynamic work process that is supposedly very different from the repetitive task that post-editing static MT output can be. This communication presents an experiment with adaptive MT conducted during the 2020-2021 academic year. As part of a course on MT and post-editing, second-year master’s students conducted group projects on the Lilt platform. The aim of the project was to explore the potential of adaptive MT in the translation workflow, and students were therefore free to opt for the texts and the languages of their choosing. In this contribution, students’ views on the MT engine are analyzed, with a focus on their interaction with the technology. The goal is to establish whether students feel more empowered or more in control of the translation process when working with adaptive MT instead of intervening at the very end of the production chain through postediting of static MT output.