Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use

Source publication / research team or educational organization described in paper

USA

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

• In-school (K-12)

Curriculum design or implementation

3 hours to a week

rubrics. Af- ter beta-testing a module of the curriculum with nine teachers and 282 students, we reviewed teacher usage data and feed- back as well as student responses. The review process sur- faced a number of; evised through review of the results of a beta test involving nine teachers and 282 students. In order to contribute to design-based re- search and curriculum development on how to teach AI in formal K-12 educati; to iteratively develop formative assessment ma- terials in collaboration with K-12 teachers, ensuring these materials are clearly aligned with competencies within AI literacy frameworks. The RAICA Curriculum The

Ethics / responsible AI, Assessment / tutoring analytics

• 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.
• Initial discussion of AI literacy assessment has focused on competency frameworks and learning standards rather than materials for classroom use.

• 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, Assessment / tutoring analytics

Language context discussed in source publication

• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Assessment support, Ethics / responsible AI education.
• AI capability focus: Ethics / responsible AI, Assessment / tutoring analytics.

• 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.

• Instructional / curriculum-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 empirical study.
• 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.
• The review process sur- faced a number of improvements to the materials to better align them with classroom teaching practice.

• The review process sur- faced a number of improvements to the materials to better align them with classroom teaching practice.

Initial discussion of AI literacy assessment has focused on competency frameworks and learning standards rather than materials for classroom use. Responsible AI for Computa- tional Action (RAICA), a constructionist AI curriculum for middle and high school students, includes assessment mate- rials to support teachers with the evaluation of student AI lit- eracy competencies in their classrooms.

• 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
• Assessment / feedback quality
• Curriculum / course design
• Teacher professional development
• Assessment support

• Completed

9-12

In-school (K-12)

Ethics / responsible AI, Assessment / tutoring analytics

Instructional / curriculum-based learning

Medium

Medium

Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use

• John Masla
• Christina Bosch
• Prerna Ravi
• Lydia Guterman
• Sarah Wharton
• Mary Cate Gustafson-Quiett
• Samar Abu Hegly
• Calvin Macatantan
• Eric Klopfer
• Cynthia Breazeal
• Hal Abelson

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

2025

10.1609/aaai.v39i28.35191

https://ojs.aaai.org/index.php/AAAI/article/view/35191

https://ojs.aaai.org/index.php/AAAI/article/view/35191/37346

028_Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use.pdf

8

Initial discussion of AI literacy assessment has focused on competency frameworks and learning standards rather than materials for classroom use. Responsible AI for Computa- tional Action (RAICA), a constructionist AI curriculum for middle and high school students, includes assessment mate- rials to support teachers with the evaluation of student AI lit- eracy competencies in their classrooms. These materials in- clude exit tickets used as formative assessments at the end of each lesson and both teacher and student-facing rubrics. Af- ter beta-testing a module of the curriculum with nine teachers and 282 students, we reviewed teacher usage data and feed- back as well as student responses. The review process sur- faced a number of improvements to the materials to better align them with classroom teaching practice. These included clarifying language and adding visual scaffolds. We present the assessment materials and iterative design process used to bridge the gap between the theoretical AI literacy competen- cies and their practical implementation in 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

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.