Recent language technology developments have disrupted the translation and interpreting professions. However, the focus has been on using more computational power and training larger language models, often neglecting the users of such technology (do Carmo and Moorkens 2022). To date, the goal of technology development has been the creation of an intelligent agent that emulates human behaviour to increase automation. As a response, a novel technology design framework has gained a foothold recently: human-centered artificial intelligence, where instead of human replacement, the aim is to produce a powerful tool that augments human capabilities, enhances performance, and empowers users, who are at all instances in supervisory control of such systems (Shneiderman 2022). If applied to machine translation (MT), we can talk about human-centered, augmented MT (HCAMT). This shift, moving from emulation to empowerment, places humans at the centre of AI/language technology. This PhD thesis presents the concept of Machine Translation User Experience (MTUX) as a way to foster HCAMT. Consequently, we conduct a longitudinal user study with 11 professional translators in the English-Spanish language combination that analyses the effects of traditional post-editing (TPE) and interactive post-editing (IPE) on MTUX, translation quality and productivity. MTUX results suggest that translators prefer IPE to TPE because they are in control of the interaction in this new form of translator-computer interaction and feel more empowered in their interaction with MT. Productivity results also suggest that translators working with IPE report a statistically significantly higher productivity than when working with TPE. Quality results also indicate that translators offer more fluent translations in IPE, and equally adequate translations in both post-editing modalities. All these results allow for reflection on the potential adoption of IPE as a more HCAMT post-editing modality, which empowers the users, who have been increasingly reluctant to interact with machine translation post-editing in industry workflows (Cadwell, O’Brien, and Teixeira 2018). This PhD thesis establishes the methodology for fostering HCAMT tools, systems and workflows through the study of MTUX. The successful implementation of HCAMT in translation and interpreting may lead to sustainable, diverse, and ethically sound development in MT systems and other technological tools through a wide variety of users and use-cases.