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Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

6

DOI

10.18260/1-2--42750

Permanent URL

https://peer.asee.org/42750

Download Count

284

Paper Authors

biography

Helen Zhang Boston College

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Helen Zhang is a senior research associate working at the Lynch School of Education, Boston College. Her research interest includes STEM education, design thinking, and AI education.

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Irene A. Lee MIT STEP Lab

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IRENE LEE is the PI of NSF ITEST Everyday AI and the NSF ITEST EAGER funded Developing AI LIteracy (DAILy) project.

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Katherine Strong Moore Massachusetts Institute of Technology

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Kate Moore is a research scientist who studies how to teach middle and high school students about systems and ethics of artificial intelligence and machine learning. She earned her doctoral degree at Teachers College, Columbia University, where she studied cooperative learning and collaborative problem solving, and worked part-time as a professional development coach for STEM teachers in New York City public schools with the Center for the Professional Education of Teachers (CPET). Before entering the world of research and design, Kate served as a middle school science and special education teacher for 10 years. She has worked in public, independent, and charter schools in New York City NY, Newark NJ, and Pittsburgh PA.

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Sheikh Ahmad Shah Boston College

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Sheikh Ahmad Shah is a 3rd year Ph.D. student in the Curriculum and Instruction Graduate Program at Boston College. His research primarily focuses on STEM education, scientific literacy, and AI literacy. He is currently working as a research assistant in the lab "Innovation in Urban Science Education" led by Dr. Mike Barnett, Professor, Boston College. He also collaborates as a research assistant with Dr. Irene Lee's team at MIT Media Lab on the "Everyday AI" project.

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Abstract

This poster reports a project entitled “Everyday AI for Youth” funded by the NSF ITEST program. The rapid expansion of Artificial Intelligence (AI) necessitates a need of educating students to become knowledgeable of AI and aware of its interrelated technical, social, and human implications. The latter (ethics) is particularly important to K-12 students because they have been Interacting with AI through everyday technology without realizing it. They may be targeted by AI generated fake content on social media and may have been victims of algorithm bias in AI applications of facial recognition and predictive policing. To empower them in the era of AI, education must support youth to recognize potential harms of AI. However, this is not easy. Ethics is complex and requires critical thinking of perspectives of various stakeholders involved in the design of AI, which is difficult for adolescents as they tend to think in an egocentric way.

In our project, we selected and sequenced a suite of ethics activities to expose students to different aspects of AI-related ethics issues. Informed by effective pedagogies and curricula to teach design ethics, these AI ethics lessons (1) stimulate students’ ethical imagination through designing algorithms for making the “best” PB&J sandwiches and imagining the definitions of “best PB&J sandwich” by different stakeholders (e.g., parents, children, dentists). By creating these personas, students begin to understand that users' priorities can change the design of the algorithm; (2) help students recognize ethical issues through investigating bias of existing technologies (e.g., Google Image search) and discussing whom the bias may impact; (3) help students analyze key ethical concepts and principles that are applicable to the AI field (e.g., the Blueprint for an AI Bill of Rights) and encourage them to take ethics seriously through case studies of how biased facial recognition technology harmed job applicants and misled police’s judgments; (4) increase student sensitivity to ethical issues by hands-on experiments of training AI models using unbalanced datasets and game playing of how deepfakes and misinformation spread out; (5) improve ethical judgment and willpower by engaging students in a culminating design project where they redesign the YouTube recommendation system. Students critiqued the technology, identified its sources of bias (e.g, selective stakeholders in the design, datasets), and created a plan outlining how to improve the system. To ensure that students develop an integrated understanding, these ethics lessons are embedded in their learning of technical aspects of AI.

Implementing these lessons showed a high engagement of all students, particularly female students of color, and an increase in their AI ethics awareness and knowledge. This suggests that our approach is highly promising in terms of preparing youth to become responsible and mindful consumers and future developers of AI technologies. Our work contributes to the AI and the design education field by providing a working learning sequence of how to teach ethical designs of AI to middle schoolers.

Zhang, H., & Lee, I. A., & Moore, K. S., & Shah, S. A. (2023, June), Board 279: Ethics in Artificial Intelligence Education: Preparing Students to Become Responsible Consumers and Developers of AI Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42750

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