Empowering Educators in AI: Insights from Co-Designing an AI Microcredential with and for K-12 Educators

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)

Course implementation or course design

one hour

ducators in AI: Insights from Co-Designing an AI Microcredential with and for K-12 Educators Nicole M. Hutchins, Shan Zhang, Joanne R. Barrett, Maya Israel University of Florida misrael@coe.ufl.edu Abstract This; the co-design process for a foundational AI microcredential course targeting K-12 teachers’ knowl- edge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers; fectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and in- structional coaches to ensure the course’s relevance and prac- ticality. Using conjecture mapping and memoing,

AI literacy / AI concepts

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• 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.
• This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers’ knowl- edge, agency, and effectiveness in integrating AI into their classrooms.

• 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.

AI literacy / AI concepts

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development.
• AI capability focus: AI literacy / AI concepts.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• 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.

• Co-design / participatory design
• 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: Co-design / participatory design.
• 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.
• We collaborated with six K-12 teachers and in- structional coaches to ensure the course’s relevance and prac- ticality.

• We collaborated with six K-12 teachers and in- structional coaches to ensure the course’s relevance and prac- ticality.

This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers’ knowl- edge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and in- structional coaches to ensure the course’s relevance and prac- ticality.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• 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
• Teacher readiness
• Curriculum / course design
• Teacher professional development
• AI literacy / AI concepts

• Completed

K-12

In-school (K-12)

AI literacy / AI concepts

Co-design / participatory design

Low to Medium

Medium

Empowering Educators in AI: Insights from Co-Designing an AI Microcredential with and for K-12 Educators

• Nicole M. Hutchins
• Shan Zhang
• Joanne R. Barrett
• Maya Isreal

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

2025

10.1609/aaai.v39i28.35186

https://ojs.aaai.org/index.php/AAAI/article/view/35186

https://ojs.aaai.org/index.php/AAAI/article/view/35186/37341

023_Empowering Educators in AI_ Insights from Co-Designing an AI Microcredential with and for K-12 Educators.pdf

8

This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers’ knowl- edge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and in- structional coaches to ensure the course’s relevance and prac- ticality. Using conjecture mapping and memoing, we system- atically captured and analyzed insights from the collaborative process. These methods helped us pinpoint essential themes and requirements for effective professional development (PD) that meets the unique challenges and opportunities of teach- ing about and using AI in K-12 classrooms. Themes included concerns about in-class monitoring for unethical impacts of AI integration and the desire for empowerment in evaluat- ing and selecting AI tools that they can best leverage to meet state and national standards. Educator requirements centered on the creation of quick, easily accessible, and asynchronous learning activities. In addition, educators requested just-in- time AI integration resources and learning opportunities that can be leveraged throughout the year, rather than being lim- ited to PD sessions. This study contributes to AI education by providing a framework for designing teacher professional development programs that are responsive to the evolving ed- ucational landscape and the specific needs of K-12 teachers.

• 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

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.