When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the stateof-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.

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This paper is about the development of systems whose end users are professional people working in a specific domain (e.g., medicine, geology, mechanical engineering); they are expert in that domain, but not necessarily expert in nor even conversant with computer science. In several work organizations, end users need to tailor their software systems to better adapt them to their requirements and even to create or modify software artifacts. These are end-user development activities and are the focus of this paper. A model of the interaction between users and systems, which also takes into account their reciprocal coevolution during system usage, is discussed. This model is used to define a methodology aimed at designing software environments that allow end users to become designers of their own tools. The methodology is illustrated by discussing two experimental cases.

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Algorithms are more and more pervading our everyday life: from automatic checkouts in supermarkets and e-banking to booking a flight online. Understanding an algorithmic solution to a problem is a very relevant activity to improve end-users' involvement. To this end, adopting a meta-design approach may help to support end-users to appropriate the design skills necessary for contributing to system design, in new and engaging modalities. By acquiring Computational Thinking (CT) skills (e.g., algorithmic thinking, abstraction), end-users will be able to understand and trust algorithms, while at the same time participate in the design and development of systems evolving in accordance with their needs. In this work, we focus on two different ways of improving CT skills: playfulness and collaboration. We introduce a game-based system, TAPASPlay, to foster CT skills and we report the results of an exploratory study with 18 users; our hypothesis is that learning CT through gameplay is effective and we tested it by involving participants in game sessions providing playful experience and collaborative learning. Keywords Computational thinking • Gameplay • Tangible user interface • Constructionist video games This is an extended and revised version of a paper that was presented at the 2017 Workshop on Games-Human Interaction [19]. This paper significantly expands over the presentation and experimental validation of TAPASPlay.