"A Key to Reducing Inequities in Like, AI, is by Reducing Inequities Everywhere First"

Everson, Jayne; Kivuva, F. Megumi; Ko, Amy J.

doi:10.1145/3478431.3499395

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Recently, K-12 AI educators have been specifically working toward integrating Culturally Responsive Computing in AI education (Solyst et al 2023;Li et al 2023). For AI education, this looks like helping students develop AI competencies, cultural competence, critical awareness of AI's role in society, and identities as tech change-makers (Everson, Kivuva, and Ko 2022;Kapor Center 2021;Madkins et al 2019;Solyst et al 2023). In alignment with these goals, we developed an AI literacy framework that builds on existing literacy frameworks and Culturally Responsive Computing.…”

Section: Addressing Diversity In Ai Literacymentioning

confidence: 99%

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Williams,

Alghowinem,

Breazeal

2024

AAAI

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Artificial Intelligence (AI) is permeating almost every area of society, reshaping how many people, including youth, navigate the world. Despite the increased presence of AI, most people lack a baseline knowledge of how AI works. Moreover, social barriers often hinder equal access to AI courses, perpetuating disparities in participation in the field. To address this, it is crucial to design AI curricula that are effective, inclusive, and relevant, especially to learners from backgrounds that are historically excluded from working in tech. In this paper, we present AI for Wellbeing, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing. We specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds. We piloted AI for Wellbeing in a 5-day virtual workshop with middle school teachers and students. Then, using a mixed-methods approach, we analyzed students' work and teachers' feedback. Our results suggest that the curriculum content and design effectively engaged students, enabling them to implement meaningful AI projects for wellbeing. We hope that the design of this curriculum and insights from our evaluation will inspire future efforts to create culturally relevant K-12 AI curricula.

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Section: Addressing Diversity In Ai Literacymentioning

confidence: 99%

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Williams,

Alghowinem,

Breazeal

2024

AAAI

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“…In general, the concept of low socio-economic status varies from including students from low-income families, underprivileged conditions, living in marginalized communities, or studying at schools with a high level of vulnerability. The courses we encountered are typically aimed at novices with short durations ranging from 3 (Zhang et al, 2022) to 6 weeks (Everson et al, 2022). Courses are applied face-to-face (Araya et al, 2021) or remotely (Zhang et al, 2022;Everson et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…The courses we encountered are typically aimed at novices with short durations ranging from 3 (Zhang et al, 2022) to 6 weeks (Everson et al, 2022). Courses are applied face-to-face (Araya et al, 2021) or remotely (Zhang et al, 2022;Everson et al, 2022). In this context, various pedagogical approaches are adopted including collaborative learning (Araya et al, 2021;Everson et al, 2022), interactive and game-based learning (Zhang et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…Courses are applied face-to-face (Araya et al, 2021) or remotely (Zhang et al, 2022;Everson et al, 2022). In this context, various pedagogical approaches are adopted including collaborative learning (Araya et al, 2021;Everson et al, 2022), interactive and game-based learning (Zhang et al, 2022). While Eguchi (2021) and Zhang et al (2022) use Google Teachable Machine as a tool, Everson et al (2022) used an AI chatbot created by the students themselves.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, various pedagogical approaches are adopted including collaborative learning (Araya et al, 2021;Everson et al, 2022), interactive and game-based learning (Zhang et al, 2022). While Eguchi (2021) and Zhang et al (2022) use Google Teachable Machine as a tool, Everson et al (2022) used an AI chatbot created by the students themselves. Others due to limitations related to the technical infrastructure apply only unplugged activities (Araya et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

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Teaching Machine Learning to Middle and High School Students from a Low Socio-Economic Status Background

Martins,

Gresse Von Wangenheim,

Rauber

et al. 2023

Informatics in Education

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Knowledge about Machine Learning is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in Brazil. Results show that these students can understand how ML works and execute the main steps of a human-centered process for developing an image classification model. No substantial differences regarding class periods, educational stage, and sex assigned at birth were observed. The course was perceived as fun and motivating, especially to girls. Despite the limitations in this context, the results show that they can be overcome. Mitigating solutions involve partnerships between social institutions and university, an adapted pedagogical approach as well as increased on-by-one assistance. These findings can be used to guide course designs for teaching ML in the context of underprivileged students from a low socio-economic status background and thus contribute to the inclusion of these students.

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Conceptualizing Approaches to Critical Computing Education: Inquiry, Design, and Reimagination

Morales-Navarro

Kafai

2023

Past, Present and Future of Computing Education Research

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"A Key to Reducing Inequities in Like, AI, is by Reducing Inequities Everywhere First"

Cited by 14 publications

References 22 publications

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Teaching Machine Learning to Middle and High School Students from a Low Socio-Economic Status Background

Conceptualizing Approaches to Critical Computing Education: Inquiry, Design, and Reimagination

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