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Making Sense of Machine Learning: Integrating Youth's Conceptual, Creative, and Critical Understandings of AI

Authors :
Morales-Navarro, Luis
Kafai, Yasmin B.
Castro, Francisco
Payne, William
DesPortes, Kayla
DiPaola, Daniella
Williams, Randi
Ali, Safinah
Breazeal, Cynthia
Lee, Clifford
Soep, Elisabeth
Long, Duri
Magerko, Brian
Solyst, Jaemarie
Ogan, Amy
Tatar, Cansu
Jiang, Shiyan
Chao, Jie
Rosé, Carolyn P.
Vakil, Sepehr
Source :
Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023
Publication Year :
2023

Abstract

Understanding how youth make sense of machine learning and how learning about machine learning can be supported in and out of school is more relevant than ever before as young people interact with machine learning powered applications everyday; while connecting with friends, listening to music, playing games, or attending school. In this symposium, we present different perspectives on understanding how learners make sense of machine learning in their everyday lives, how sensemaking of machine learning can be supported in and out of school through the construction of applications, and how youth critically evaluate machine learning powered systems. We discuss how sensemaking of machine learning applications involves the development and integration of conceptual, creative, and critical understandings that are increasingly important to prepare youth to participate in the world.

Details

Database :
arXiv
Journal :
Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023
Publication Type :
Report
Accession number :
edsarx.2305.02840
Document Type :
Working Paper