Investigating Youths' Everyday Understanding of Machine Learning Applications: a Knowledge-in-Pieces Perspective
CoRR(2024)
Abstract
Despite recent calls for including artificial intelligence (AI) literacy in
K-12 education, not enough attention has been paid to studying youths' everyday
knowledge about machine learning (ML). Most research has examined how youths
attribute intelligence to AI/ML systems. Other studies have centered on youths'
theories and hypotheses about ML highlighting their misconceptions and how
these may hinder learning. However, research on conceptual change shows that
youths may not have coherent theories about scientific phenomena and instead
have knowledge pieces that can be productive for formal learning. We
investigate teens' everyday understanding of ML through a knowledge-in-pieces
perspective. Our analyses reveal that youths showed some understanding that ML
applications learn from training data and that applications recognize patterns
in input data and depending on these provide different outputs. We discuss how
these findings expand our knowledge base and implications for the design of
tools and activities to introduce youths to ML.
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