Neurodevelopmental Disorders (NDDs) represent a significant healthcare and economic burden for families and society. Technology, including AI and digital technologies, offers potential solutions for the assessment, monitoring, and treatment of NDDs. However, further research is needed to determine the effectiveness, feasibility, and acceptability of these technologies in NDDs, and to address the challenges associated with their implementation. In this work, we present the application of social robotics using a Pepper robot connected to the OpenAI system (Chat-GPT) for real-time dialogue initiation with the robot. After describing the general architecture of the system, we present two possible simulated interaction scenarios of a subject with Autism Spectrum Disorder in two different situations. Limitations and future implementations are also provided to provide an overview of the potential developments of interconnected systems that could greatly contribute to technological advancements for Neurodevelopmental Disorders (NDD).

Today georeferenced images posted on the social network provide a lot of information about people behaviours and movements. Using social media platforms users upload photos, share locations and post comments about their activities, influencing other people. In this research, we examine the relationship between human mobility and touristic attractions through geo-located images provided by Flickr users. A sample of 26,392 pictures related to 6 Italian cities has been collected and analysed applying cluster analysis. In our work, the function of the clustering analysis, employed in Wolfram Mathematica Machine Learning, allows one to automatically identify clusters surrounding points of interest (POIs). Findings show that social media datasets are valuable data to understand tourist behaviour and mobility within a location. The scope is to delineate famous or unpopular places and propose new touristic scenarios, highlighting how the social part covers the main role in the POIs’ recommendation process in the touristic field. Furthermore, we aim to promote the machine learning approach as a useful support in human behaviour research.