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Title: Exploring Generative Models with Middle School Students
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 efforts 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 benefits and harms, while reflecting on their ethical implications. Learning from our findings, we propose an improved learning trajectory for complex socio-technical systems.  more » « less
Award ID(s):
2022502
PAR ID:
10252918
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Page Range / eLocation ID:
1 to 13
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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