SPEAKY Project: Adaptive Tutoring System based on Reinforcement Learning for Driving Exercizes and Analysis in ASD Children

Moussa Nasir , Linda Fellus , Alexandre Pitti (1)

1 Neurocybernétique

Intelligent tutoring systems are increasingly effective for helping the teacher's work with children. However, these technologies are still poorly used for cognitively impaired infants who display autistic spectrum disorders and intellectual disabilities as they don't adapt easily to each infant. We propose an adaptive learning system called SPEAKY for assisting the learning of lexicon to children with the help of the tutor. SPEAKY present a pair of images and questions of gradual difficulty to each infant and adapt the set of images and questions with respect to the child response. Depending on how their tutors scored the child's response, SPEAKY modifies its model of the learner. We proposed an approach based on the reinforcement learning in order to adapt exercises' difficulty to the level and profile of one infant. Our database contains more than 300 images and we have asked more than 2000 questions in three weeks considering all the exercise sessions. The results confirmed that generalization is not possible and that adaptiveness is important as we found that difficulty is child-specific. Through the results we gathered, we also determine the difficulties and facilities points of each child.

Domaines: Robotique [cs.RO]; Environnements Informatiques pour l'Apprentissage Humain; Machine Learning [stat.ML]; Linguistique; Psychologie

Moussa Nasir, Linda Fellus, Alexandre Pitti. SPEAKY Project: Adaptive Tutoring System based on Reinforcement Learning for Driving Exercizes and Analysis in ASD Children. ICDL-EpiRob 2018 Workshop on “Understanding Developmental Disorders: From Computational Models to Assistive Technologies", Sep 2018, Tokyo, Japan. ⟨hal-01976660⟩