Exploring generative models with middle school students

Safnah Ali, Daniella DiPaola, Irene Lee, Jenna Hong, Cynthia Breazeal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationCHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationMaking Waves, Combining Strengths
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450380966
DOIs
StatePublished - May 6 2021
Event2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021 - Virtual, Online, Japan
Duration: May 8 2021May 13 2021

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Country/TerritoryJapan
CityVirtual, Online
Period5/8/215/13/21

Keywords

  • Ai education
  • Artifcial intelligence
  • Generative adversarial networks
  • Generative machine learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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