AI Audit: A Card Game to Reflect on Everyday AI Systems

Source publication / research team or educational organization described in paper

Not specified in extracted text

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Curriculum design or implementation

Not specified in extracted text

cade has seen a surge in AI literacy courses, tools and teaching programs for K-12 students. In 2018, AAAI and CSTA developed national guidelines for teaching AI to K-12 students, where they outlined 5 big ideas; ents. In 2018, AAAI and CSTA developed national guidelines for teaching AI to K-12 students, where they outlined 5 big ideas that all stu- dents must know: computers perceive the world using sen- sors, agents ma

Explainable AI / robustness, Ethics / responsible AI

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

9-12

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Explainable AI / robustness, Ethics / responsible AI

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Teacher professional development, Outreach / informal learning, Ethics / responsible AI education.
• AI capability focus: Explainable AI / robustness, Ethics / responsible AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Unplugged learning, Game-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Unplugged learning, Game-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• Pre- vious work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing respon- sible AI.

• Pre- vious work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing respon- sible AI.
• There is little work in using game-based learning methods in AI literacy.
• In this work, we developed a competitive card game for middle and high school students called “AI Audit” where they play as AI start-up founders building novel AI-powered technol- ogy.

An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Pre- vious work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing respon- sible AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• AI literacy
• Conceptual understanding
• Ethics and responsible use
• Teacher readiness
• Curriculum / course design
• Learning tool / resource design
• Teacher professional development
• Outreach / informal learning

• Completed

9-12

In-school (K-12)

Explainable AI / robustness, Ethics / responsible AI

Unplugged learning, Game-based learning

Medium

Medium

AI Audit: A Card Game to Reflect on Everyday AI Systems

• Safinah Ali
• Vishesh Kumar
• Cynthia Breazeal

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26897

https://ojs.aaai.org/index.php/AAAI/article/view/26897

https://ojs.aaai.org/index.php/AAAI/article/view/26897/26669

088_AI Audit_ A Card Game to Reflect on Everyday AI Systems.pdf

9

An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Pre- vious work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing respon- sible AI. There is little work in using game-based learning methods in AI literacy. Games are known to be compelling media to teach children about complex STEM concepts. In this work, we developed a competitive card game for middle and high school students called “AI Audit” where they play as AI start-up founders building novel AI-powered technol- ogy. Players can challenge other players with potential harms of their technology or defend their own businesses by fea- tures that mitigate these harms. The game mechanics reward systems that are ethically developed or that take steps to mit- igate potential harms. In this paper, we present the game de- sign, teacher resources for classroom deployment and early playtesting results. We discuss our reflections about using games as teaching tools for AI literacy in K-12 classrooms.

• In-school (K-12)

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.