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About

Theo Huibers is a leading researcher at the intersection of child-computer interaction, conversational AI, and information retrieval. Their work focuses on a critical challenge: how children interact with and trust social robots as information sources. Huibers’ major contribution lies in empirically investigating children’s trust calibration—ensuring young users neither over-trust nor under-trust robotic agents. Their most cited work (7 citations) reveals how children’s attitudes toward robots affect their acceptance of provided information, a foundational insight for designing responsible AI. Through the CHATTERS project, Huibers has pioneered methods for designing conversational robots that foster appropriate trust relationships, particularly during the pandemic. Their research uniquely combines speech analysis—identifying trust indicators in children’s natural language—with practical robot design for educational and cultural heritage contexts. By addressing children’s difficulties in assessing information credibility, Huibers’ work directly impacts the development of safer, more effective digital assistants for young users. Their ongoing exploration of real-time trust measures promises to transform how we build responsible, child-centered conversational agents.