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Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
Jin Huang, Eric Nichols, Fethiye Irmak Doğan, Hatice Güneş ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This paper discusses technical frameworks for personalizing social robots used in clinical settings to support child well-being. It frames personalization as a recommendation problem and identifies data collection challenges specific to working with children, such as changing states, weak outcome signals, and limited repeat visits. The authors propose four data principles to guide collection: integrated profiles, effectiveness signals, linkable coverage, and exposure records. The work is theoretical and focused on data engineering rather than evaluating actual interventions with children.
Key points
- Personalization of social robot actions for children can be framed using recommender-system approaches
- Key data challenges include children's changing states within and across visits, weak indirect signals of intervention effectiveness, and rarity of repeat visits
- Anonymization creates a barrier to linking data across sessions needed for personalization
- Observational data is inherently biased toward existing practices since care cannot be randomized
- Four proposed data principles: integrated profile, effectiveness signals, linkable coverage, and exposure record logging
Limitations
This is a theoretical framework paper that does not evaluate actual interventions or outcomes with children. It does not present empirical evidence about whether personalized social robots actually improve child well-being, only discusses the technical and methodological requirements for building such systems.
Abstract
Social robots are increasingly deployed in clinical settings to support the well-being of children, where effective support must be personalized to each child. Personalization, choosing the robot action best suited to each child, can be framed as a recommendation problem, and a recently proposed recommender-system framework for social robots offers a principled approach through user profiling, ranking, and responsible computing. Instantiating it, however, is blocked not by the model but by the data, which is hard to gather. A child's state shifts within and across visits, so no fixed description of the user holds. Within a session, the few signals of whether the robot's actions helped are weak and indirect. Across sessions, children are rarely seen more than once, and anonymization breaks the identity needed to link visits. Because care cannot be randomized, existing data is observational, biased toward whatever was already done. Each is a familiar recommender-system problem, and we propose four data principles in response: an integrated profile, effectiveness signals, linkable coverage, and an exposure record logged at collection time. We identify which of these principles each capability requires, and frame them as concrete guidelines for data collection.