“How Can I Code A.I. Responsibly?”: The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I.

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)

Workshop / professional learning activity

one-day workshop to learn and practice the computational ac- tion process; one-day workshop to learn and practice the computational action process

“How Can I Code A.I. Responsibly?”: The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I. H. Nicole Pang, Robert Parks, Cynthia Breazeal*, Hal Abelson* Massachus; ople about the societal impact of A.I. that goes one step further: empowering K-12 students to use tools and frameworks to create socially responsible A.I. The computational action process is a curriculum and to

Ethics / responsible AI

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-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.
• Teaching young people about artificial intelligence (A.I.) is recognized globally as an important educational effort by or- ganizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12.

• 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.

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, Ethics / responsible AI education.
• AI capability focus: 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.

• Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published empirical study.
• Pedagogical pattern: Hands-on / experiential 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.
• In a human-subject research study, 101 U.S. and in- ternational students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational ac- tion process.

• In a human-subject research study, 101 U.S. and in- ternational students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational ac- tion process.

Teaching young people about artificial intelligence (A.I.) is recognized globally as an important educational effort by or- ganizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12. A common theme among K-12 A.I. ed- ucation programs is teaching how A.I. can impact society in both positive and negative ways.

• 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.

• Survey
• 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.

• Conceptual understanding
• Ethics and responsible use
• Curriculum / course design
• Learning tool / resource design
• Ethics / responsible AI education
• Ethics / responsible AI

• Completed

K-12

In-school (K-12)

Ethics / responsible AI

Hands-on / experiential learning

Medium

Medium

“How Can I Code A.I. Responsibly?”: The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I.

• H. Nicole Pang
• Robert Parks
• Cynthia Breazeal
• Hal Abelson

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

2023

10.1609/aaai.v37i13.26902

https://ojs.aaai.org/index.php/AAAI/article/view/26902

https://ojs.aaai.org/index.php/AAAI/article/view/26902/26674

093_#U201cHow Can I Code A.I. Responsibly_#U201d_ The Effect of Computational Action on K-12 Students Learning and Creating Socially Responsible A.I.pdf

8

Teaching young people about artificial intelligence (A.I.) is recognized globally as an important educational effort by or- ganizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12. A common theme among K-12 A.I. ed- ucation programs is teaching how A.I. can impact society in both positive and negative ways. We present an effective tool that teaches young people about the societal impact of A.I. that goes one step further: empowering K-12 students to use tools and frameworks to create socially responsible A.I. The computational action process is a curriculum and toolkit that gives students the lessons and tools to evaluate positive and negative impacts of A.I. and consider how they can cre- ate beneficial solutions that involve A.I. and computing tech- nology. In a human-subject research study, 101 U.S. and in- ternational students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational ac- tion process. Pre-post questionnaires measured on the Likert scale students’ perception of A.I. in society and students’ de- sire to use A.I. in their projects. Analysis of the results shows that students who identified as female agreed more strongly with having a concern about the impacts of A.I. than those who identified as male. Students also wrote open-ended re- sponses to questions about what socially responsible tech- nology means to them pre- and post-study. Analysis shows that post-intervention, students were more aware of ethical considerations and what tools they can use to code A.I. re- sponsibly. In addition, students engaged actively with tools in the computational action toolkit, specifically the novel impact matrix, to describe the positive and negative impacts of A.I. technologies like facial recognition. Students demonstrated breadth and depth of discussion of various A.I. technologies’ far-reaching positive and negative impacts. These promising results indicate that the computational action process can be a helpful addition to A.I. education programs in furnishing tools for students to analyze the effects of A.I. on society and plan how they can create and use socially responsible A.I.

• In-school (K-12)

• 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

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.