Museums and the Metaverse: Emerging Technologies to Promote Inclusivity and Engagement

Over the past two decades, museums have increasingly sought to build connections with the community and increase inclusivity of visitors. At the same time, emerging technologies, such as extended reality (XR) and virtual museums (VM) are increasingly adopted to engage with different generational expectations but also for the purposes of supporting inclusivity and neurodiverse populations. First such technologies were adopted to augment exhibitions in the physical museum space for edutainment. Since then, XR has expanded from room-size environments (CAVEs) and augmented exhibitions to the creation of entire virtual museums, such as The Museum of Pure Form and The Virtual Museum of Sculpture. Digital twins of museums are increasingly common, along with UNESCO World Heritage Sites. Such virtual experiences can be leveraged to prepare neurodiverse visitors prior to visiting a museum. This chapter will outline how existing approaches to social stories and sensory maps may be combined with XR experiences to support neurodiverse visitors and their families. While onsite, immersive technologies can be used both for engagement and to provide accommodations for greater inclusivity and diversity

Personalized Recommendation of PoIs to People with Autism

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model