We Are AI: Taking Control of Technology

Source publication / research team or educational organization described in paper

USA

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

• Higher education

Course implementation or course design

2023 session successfully facilitated the course in Fall 2023; 3 weeks or spread out to one module per week

Not specified in extracted text

Ethics / responsible AI

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Higher education

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.
• Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable—benefiting diverse stakeholders while control- ling the risks.

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

• 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.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Instructional / curriculum-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.
• This paper intro- duces “We are AI: Taking Control of Technology,” a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting.

• This paper intro- duces “We are AI: Taking Control of Technology,” a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting.
• We also discuss two offerings of We are AI to an active and engaged group of librarians and professional staff at New York University, highlighting suc- cesses and areas for improvement.

Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable—benefiting diverse stakeholders while control- ling the risks. Achieving this goal requires active engagement and participation from the broader public.

• 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
• Engagement / motivation
• Ethics and responsible use
• Curriculum / course design
• Ethics / responsible AI education
• Ethics / responsible AI

• Completed

Higher education

Higher education

Ethics / responsible AI

Instructional / curriculum-based learning

Medium

Low to Medium

We Are AI: Taking Control of Technology

• Julia Stoyanovich
• Armanda Lewis
• Eric Corbett
• Lucius E.J. Bynum
• Lucas Rosenblatt
• Falaah Arif Khan

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35178

https://ojs.aaai.org/index.php/AAAI/article/view/35178

https://ojs.aaai.org/index.php/AAAI/article/view/35178/37333

015_We Are AI_ Taking Control of Technology.pdf

8

Responsible AI (RAI) is the science and practice of ensuring the design, development, use, and oversight of AI are socially sustainable—benefiting diverse stakeholders while control- ling the risks. Achieving this goal requires active engagement and participation from the broader public. This paper intro- duces “We are AI: Taking Control of Technology,” a public education course that brings the topics of AI and RAI to the general audience in a peer-learning setting. We outline the goals behind the course’s development, dis- cuss the multi-year iterative process that shaped its creation, and summarize its content. We also discuss two offerings of We are AI to an active and engaged group of librarians and professional staff at New York University, highlighting suc- cesses and areas for improvement. The course materials, in- cluding a multilingual comic book series by the same name, are publicly available and can be used independently. By sharing our experience in creating and teaching We are AI, we aim to introduce these resources to the community of AI edu- cators, researchers, and practitioners, supporting their public education efforts.

• Higher education

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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

• group_size

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.