This article describes a co-design process in the context of user experience (UX) and usability testing and analysis of a first proof of concept of e-collaboration features based on unified communications, co-designed within an organization aiming to optimize users' communication cognitive load. An initial digital prototype with a detailed graphical interface, and simulated user narratives was established and the qualitative validation process is described and discussed. The implemented R&D process is mainly supported on user-centred design (UCD) methodology, namely action research with service design thinking method and co-design techniques. Qualitative data was gathered with concurrent think-aloud activities (CTA) stimulated by user experience expectation questions, observation notes, with integration in an eye tracking technology system. The UCD process and results are discussed, substantiating the added value due to the individual contributions and consequent usefulness of a final unified communication service for the organization.

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The increasing maturity of artificial intelligence technologies such as Machine Learning algorithms, Natural Language Processing (NLP), Automatic Speech Recognition (ASR) and Natural Language generation are changing the way users interact with technology. Specifically, as voice interactions are becoming commonplace, it is important to understand how such systems are being trained. This systematic review investigates how human data is collected for training conversational agents, with specific interest on data sets directly obtained from human participation in real contexts of need and use. The work reported in this article was supported by PRISMA guidelines and search procedures were led in Scopus, Web of Science and ProQuest, in English and within the last 15-years (2005-2020), with pre-defined criteria to get a detailed holistic perspective of practices published until July 2020. From both search iterations, a total of 22 papers were considered for this review. The main contributions from these papers reveal a common use of learning from demonstration/observation and crowdsourcing methods, in system training and dataset cataloguing, alongside handwriting and sentence labelling and Wizard-of-Oz based studies.