Towards Building Child-Centered Machine Learning Pipelines: Use Cases from K-12 and Higher-Education

This paper presents a framework for building child-centered machine learning pipelines and describes two case studies: one predicting classroom engagement levels using video and biosensor data to support teachers, and another developing a math learning game with handwriting recognition for K-12 students.

Researchers and policy-makers have started creating frameworks and guidelines for building machine-learning (ML) pipelines with a human-centered lens. Machine Learning pipelines stand for all the necessary steps to develop ML systems (e.g., developing a predictive keyboard). On the other hand, a child-centered focus in developing ML systems has been recently gaining interest as children are becoming users of these products. These efforts dominantly focus on children's interaction with ML-based s

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