TY - GEN
T1 - Exploring generative models with middle school students
AU - Ali, Safnah
AU - DiPaola, Daniella
AU - Lee, Irene
AU - Hong, Jenna
AU - Breazeal, Cynthia
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - Applications of generative models such as Generative Adversarial Networks (GANs) have made their way to social media platforms that children frequently interact with. While GANs are associated with ethical implications pertaining to children, such as the generation of Deepfakes, there are negligible eforts to educate middle school children about generative AI. In this work, we present a generative models learning trajectory (LT), educational materials, and interactive activities for young learners with a focus on GANs, creation and application of machine-generated media, and its ethical implications. The activities were deployed in four online workshops with 72 students (grades 5-9).We found that these materials enabled children to gain an understanding of what generative models are, their technical components and potential applications, and benefts and harms, while refecting on their ethical implications. Learning from our fndings, we propose an improved learning trajectory for complex socio-technical systems.
AB - Applications of generative models such as Generative Adversarial Networks (GANs) have made their way to social media platforms that children frequently interact with. While GANs are associated with ethical implications pertaining to children, such as the generation of Deepfakes, there are negligible eforts to educate middle school children about generative AI. In this work, we present a generative models learning trajectory (LT), educational materials, and interactive activities for young learners with a focus on GANs, creation and application of machine-generated media, and its ethical implications. The activities were deployed in four online workshops with 72 students (grades 5-9).We found that these materials enabled children to gain an understanding of what generative models are, their technical components and potential applications, and benefts and harms, while refecting on their ethical implications. Learning from our fndings, we propose an improved learning trajectory for complex socio-technical systems.
KW - Ai education
KW - Artifcial intelligence
KW - Generative adversarial networks
KW - Generative machine learning
UR - http://www.scopus.com/inward/record.url?scp=85106680425&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106680425&partnerID=8YFLogxK
U2 - 10.1145/3411764.3445226
DO - 10.1145/3411764.3445226
M3 - Conference contribution
AN - SCOPUS:85106680425
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Y2 - 8 May 2021 through 13 May 2021
ER -