The CoWriter activity involves a child in a rich and complex interaction where he has to teach handwriting to a robot. The robot must convince the child it needs his help and it actually learns from his lessons. To keep the child engaged, the robot must learn at the right rate, not too fast otherwise the kid will have no opportunity for improving his skills and not too slow otherwise he may loose trust in his ability to improve the robot’ skills. We tested this approach in real pedagogic/therapeutic contexts with children in difficulty over repeated long sessions (40-60 min). Through 3 different case studies, we explored and refined experimental designs and algorithms in order for the robot to adapt to the troubles of each child and to promote their motivation and self-confidence. We report positive observations, suggesting commitment of children to help the robot, and their comprehension that they were good enough to be teachers, overcoming their initial low confidence with handwriting.
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Robots for education are not limited to support ICT teaching, and they are indeed finding new roles in the classroom. This article reports on such a new paradigm for educative robots, that involves learning by teaching and strong social engagement to help children struggling with handwriting. Our system relies on machine-learning and child-robot interaction with a small humanoid robot, and we present several real-world studies in schools and with occupational therapists that led us to promising initial results.