Background: Observation of child behaviour provides valuable clinical information but often requires rigorous, tedious, repetitive and time expensive protocols. For this reason, tests requiring significant time for administration and rating are rarely used in clinical practice, however useful and effective they are. This article shows that Artificial Intelligence (AI), designed to capture and store the human ability to perform standardised tasks consistently, can alleviate this problem.
Case study: We demonstrate how an AI-powered version of the Manchester Child Attachment Story Task can identify, with over 80% concordance, children with insecure attachment aged between 5 and 9 years.
Discussion: We discuss ethical issues to be considered if AI technology is to become a useful part of child mental health assessment and recommend practical next steps for the field.
Keywords: Attachment; assessment; behavioural measures; child behavioural observation; social artificial intelligence.
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