Datasets for Artificial Intelligence in Education: The Case of Children with Neurodevelopmental Disorders

Abstract

Artificial intelligence has shown promise for supporting children with neurodevelopmental disorders (NDDs) in educational settings. For such vulnerable population, aspects such as emotion, communication, and motivation are very relevant, but also challenging to be modeled. In this work, we focus on the machine learning technology used in such scenarios, in particular the characteristics of datasets used for model training. We do this by analyzing recent papers on children with NDDs. This will give insight into existing trade-offs, such as data annotation involved in data collection, as well as automation aspects. We also analyze opportunities offered by the functionalities of ML models trained on such datasets. In addition, we point out limitations and future challenges to help advance the area.