Contents

This first workshop on the theme offers a overview of the issues raised by learning in future smart cities.
The first contribution starts with theoretical considerations intended to foster a reflection of smart city education that should be not seen any longer as “infrastructure & service” but rather as a founding process, through which the relationships between persons and the inhabited territories are continuously reshaped; the paper continues, then, with the description of a strategic and methodological approach that focuses on 'museal field' and narrative as key elements of future 'learning from smart cities' and, of course, of advanced integrated technological environments designed to support it.
The second contribution discusses the role played by context in promoting engagement and exploration in situated learning experiences during field trips. In particular the authors consider field trips where children engage with the physical and social environment in order to learn about cultural and social aspects of the city they live in. By drawing on empirical data collected by means of qualitative methods, they show how learning unfolds along trajectories of experience towards pre-defined and emerging learning objectives. A reflection on the role played by technology in supporting learning experiences outside the classroom concludes the essay.
The third contribution presents a case history: a virtual museum introducing the interactive VR and MEMS applications related to the learning of chaos and complexity theory. The authors suggest that such museum can be used in the city in order to create new ways of experiencing science, turning physical activities into virtual ones. In conclusion, a possible road toward pervasive museum for smart cities.
Finally, the fourth contribution offers a completely different perspective and focuses on just-in-time and efficient support to learning for professionals working in the 'Smart city'. The authors present principle and structure of a contextual mobile learning system, which uses a search engine to find appropriate learning units in relation with working activities and worker’s profile.