Surveys with professional translators show that there is still a lot of frustration with translation tools, especially when it comes to the integration with machine translation (MT) in classic environments. As new MT integrations are developed, it becomes important to study user experience of a variety of users. During the final assignment for a postgraduate course on machine translation and post-editing, I let students reflect on features in different translation environments. Students are introduced to Lilt, currently the only translation environment to offer interactive and adaptive MT, and are asked to compare its features to those of other environments they are familiar with. Since the integration of interactive and adaptive MT are integral to the tool, students are explicitly asked to reflect on the quality of the MT output and the perceived usefulness of these features. To better assess the adaptivity of the system, students translated two texts using Lilt: one where the MT system is additionally trained on a relevant translation memory, one with the generic MT system. In this chapter, I analyse student reports from four academic years. Results show that Lilt’s minimalist interface and particularly the interactive and adaptive elements are received positively by the students, and that Lilt could be a good tool to include in future translation technology courses, perhaps especially for students less experienced with translation technology.