Familiarizing children with atificial intelligence

Abstract. Studies regarding the digital literacy of children can be found easily. Such as teaching children about coding, involvement of children in the design and development of technology, learning of CT, and abstraction. On the other hand, the availability of literature regarding the combination of children and AI is still not enough. Especially, there is a lack of research regarding AI literacy of children which is the research problem. The gap was found while searching for material regarding AI and children through ACM Digital Library and IEEE Xplore which motivated to conduct this research. Thus, the research was conducted with the aim of familiarizing children with the AI. Moreover, the qualitative research method was used for this study. The reason to choose this method was the lack of literature in this field. Another reason was to obtain evidence-based on observations in the real environment. Data was collected in the form of observations, texts (activity worksheets), pictures, video, and audio. The teacher was interviewed at the end of the last session to get feedback about children’s learning. Also, the study was conducted at an international school in Oulu, Finland. Sessions were conducted on 19 Nov and 26 Nov 2019. Each session was of approximately 45 minutes. Children belonging to the age-group of 11–12 years were included. To introduce AI to the children existing material with modification was used. During the sessions, children had some hands-on activities such as an online ML activity. Some activity worksheets were also distributed among them. Children were asked about AI before and after this concept was explained to them. Findings of the study suggested that some children’s opinion about AI was changed after they were being engaged in learning activities. In the beginning, upon asking them about AI a few children answered as coding or robot whereas repeating the same question at the end some students mentioned “thinking by itself”. In contrast, some students still mentioned robot or computer. Observations also suggest that children seemed to learn more easily through hands-on activities and by listening to stories. Based on the results of this study, it seems that more sessions with careful planning are needed to get better results in the future. One limitation is, the results of this study cannot be applied to a large group of children. Another limitation of this study is the unknown background of participants

Artificial Intelligence and Robotics in Education

This contribution aims to focus attention on the research that the working group of the Department of Educational Sciences of the University of Bologna is developing in the field of Artificial Intelligence and Robotics (AIR). In particular, the research group is developing two lines: AIR for Learning with a focus on learning processes and levels of personalization supported by AI and ER; Learning for AIR with a focus on AI and Robotics education and the need to integrate the school curriculum

Social/dialogical roles of social robots in supporting children's learning of language and literacy - A review and analysis of innovative roles

One of the many purposes for which social robots are designed is education, and there have been many attempts to systematize their potential in this field. What these attempts have in common is the recognition that learning can be supported in a variety of ways because a learner can be engaged in different activities that foster learning. Up to now, three roles have been proposed when designing these activities for robots: as a teacher or tutor, a learning peer, or a novice. Current research proposes that deciding in favor of one role over another depends on the content or preferred pedagogical form. However, the design of activities changes not only the content of learning, but also the nature of a human–robot social relationship. This is particularly important in language acquisition, which has been recognized as a social endeavor. The following review aims to specify the differences in human–robot social relationships when children learn language through interacting with a social robot. After proposing categories for comparing these different relationships, we review established and more specific, innovative roles that a robot can play in language-learning scenarios. This follows Mead’s (1946) theoretical approach proposing that social roles are performed in interactive acts. These acts are crucial for learning, because not only can they shape the social environment of learning but also engage the learner to different degrees. We specify the degree of engagement by referring to Chi’s (2009) progression of learning activities that range from active, constructive, toward interactive with the latter fostering deeper learning. Taken together, this approach enables us to compare and evaluate different human–robot social relationships that arise when applying a robot in a particular social role