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4th ICMI Workshop on Bridging Social Sciences and AI for Understanding Child Behaviour

Authors: Heysem Kaya; Anouk Neerincx; Maryam Najafian; Saeid Safavi;

4th ICMI Workshop on Bridging Social Sciences and AI for Understanding Child Behaviour

Abstract

Analysing and understanding child behaviour is a topic of great scientific interest across a wide range of disciplines, including social sciences and artificial intelligence (AI). Knowledge in these diverse fields is not yet integrated to its full potential. The aim of this workshop is to bring researchers from these fields together. The first three workshops had a significant impact. In this workshop, we discussed topics such as the use of AI techniques to better examine and model interactions and children’s emotional development, analyzing head movement patterns with respect to child age. The 2023 edition of the workshop is a successful new step towards the objective of bridging social sciences and AI, attracting contributions from various academic fields on child behaviour analysis. We see that atypical child development holds an important space in child behaviour research. While in visual domain, gaze and joint attention are popularly studied; speech and physiological signals of atypically developing children are shown to provide valuable cues motivating future work. This document summarizes the WoCBU’23 workshop, including the review process, keynote talks and the accepted papers.

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Keywords

human computer interaction, child computer interaction, parent child interaction, child psychology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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