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About

Daniel Preciado is a leading researcher at the intersection of human-robot interaction, developmental psychology, and educational technology. His work focuses on designing social robots that can support children’s learning, emotional well-being, and clinical care. Preciado’s most influential study, “Social Robots for (Second) Language Learning in (Migrant) Primary School Children” (2021, 66 citations), demonstrated that robots can outperform tablets in boosting engagement and language acquisition among young learners. He has also pioneered the use of robots to mitigate pain and anxiety during pediatric blood draws (2021, 31 citations), showing tangible clinical benefits. His theoretical contribution, the “Theory of Affective Bonding” (2025), provides a framework for understanding how humans form emotional connections with artificial beings. Preciado’s work on trust and stress reduction in classrooms (2022, 19 citations) further underscores the potential of robots as supportive companions. Notably, his innovative approach to robot programming, inspired by dog training (2024, 3 citations), makes robot teaching intuitive for non-experts. With over 127 citations across his key papers, Preciado is shaping the future of socially assistive robotics, making technology more accessible, empathetic, and effective for children.