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Emotional artificial intelligence (AI), i.e., affective computing technologies, is rapidly reshaping the education of young minds worldwide. In Japan, government and commercial stakeholders are promulgating emotional AI not only as a neoliberal, cost-saving benefit but also as a heuristic that can improve the learning experience at home and in the classroom. Nevertheless, critics warn of a myriad of risks and harms posed by the technology such as privacy violation, unresolved deeper cultural and systemic issues, machinic parentalism as well as the danger of imposing attitudinal conformity. This study brings together the Technological Acceptance Model and Moral Foundation Theory to examine the cultural construal of risks and rewards regarding the application of emotional AI technologies. It explores Japanese citizens' perceptions of emotional AI in education and children's toys via analysis of a final sample of 2000 Japanese respondents with five age groups (20s-60s) and two sexes equally represented. The linear regression models for determinants of attitude toward emotional AI in education and in toys account for 44 % and 38 % variation in the data, respectively. The analyses reveal a significant negative correlation between attitudes toward emotional AI in both schools and toys and concerns about privacy violations or the dystopian nature of constantly monitoring of children and students' emotions with AI (Education: ßDystopianConcern = - .094***; Toys: ßPrivacyConcern = - .199***). However, worries about autonomy and bias show mixed results, which hints at certain cultural nuances of values in a Japanese context and how new the technologies are. Concurring with the empirical literature on the Moral Foundation Theory, the chi-square (Χ2) test shows Japanese female respondents express more fear regarding the potential harms of emotional AI technologies for the youth's privacy, autonomy, data misuse, and fairness (p < 0.001). The policy implications of these results and insights on the impacts of emotional AI for the future of human-machine interaction are also provided.

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Observing how children manipulate objects while they are playing can help detect possible autism spectrum disorders (ASD) at an early stage. For this purpose, specialists seek the so-called "red-flags" of motor signature of ASD for more precise diagnostic tests. However, a significant drawback to achieve this is that the observation of object manipulation by the child very often is not naturalistic, as it involves the physical presence of the specialist and is typically performed in hospitals. In this framework, we present a novel Internet of Things support in the form factory of a smart toy that can be used by specialists to perform indirect and non-invasive observations of the children in naturalistic conditions. While they play with the toy, children can be observed in their own environment and without the physical presence of the specialist. We also present the technical validation of the technology and the study protocol for the refinement of the diagnostic practice based on this technology.

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BACKGROUND: EDUCERE ("Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders") is an ecosystem for ubiquitous detection, care, and early stimulation of children with developmental disorders. The objectives of this Spanish government-funded research and development project are to investigate, develop, and evaluate innovative solutions to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. Thirty multidisciplinary professionals and three nursery schools worked in the EDUCERE project between 2014 and 2017 and they obtained satisfactory results. Related to EDUCERE, we found studies based on providing networks of connected smart objects and the interaction between toys and social networks. OBJECTIVE: This research includes the design, implementation, and validation of an EDUCERE smart toy aimed to automatically detect delays in psychomotor development. The results from initial tests led to enhancing the effectiveness of the original design and deployment. The smart toy, based on stackable cubes, has a data collector module and a smart system for detection of developmental delays, called the EDUCERE developmental delay screening system (DDSS). METHODS: The pilot study involved 65 toddlers aged between 23 and 37 months (mean=29.02, SD 3.81) who built a tower with five stackable cubes, designed by following the EDUCERE smart toy model. As toddlers made the tower, sensors in the cubes sent data to a collector module through a wireless connection. All trials were video-recorded for further analysis by child development experts. After watching the videos, experts scored the performance of the trials to compare and fine-tune the interpretation of the data automatically gathered by the toy-embedded sensors. RESULTS: Judges were highly reliable in an interrater agreement analysis (intraclass correlation 0.961, 95% CI 0.937-0.967), suggesting that the process was successful to separate different levels of performance. A factor analysis of collected data showed that three factors, trembling, speed, and accuracy, accounted for 76.79% of the total variance, but only two of them were predictors of performance in a regression analysis: accuracy (P=.001) and speed (P=.002). The other factor, trembling (P=.79), did not have a significant effect on this dependent variable. CONCLUSIONS: The EDUCERE DDSS is ready to use the regression equation obtained for the dependent variable "performance" as an algorithm for the automatic detection of psychomotor developmental delays. The results of the factor analysis are valuable to simplify the design of the smart toy by taking into account only the significant variables in the collector module. The fine-tuning of the toy process module will be carried out by following the specifications resulting from the analysis of the data to improve the efficiency and effectiveness of the product.

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