Advancing Research on Equitable AI Education Through a Focus on Implementation: Insights from a Middle School Computer Vision Module Beta-Test

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)
• Higher education

Curriculum design or implementation

15 hours of instructional time total

a from RAICA’s computer vision module beta-test. Twelve educators working with ~282 students across nine pilot sites in four countries used a bespoke fidelity of implementation data collection tool (pre-made comm; address tensions and calls to ac- tion raised in literature on teaching AI to K-12 learners (e.g., Grover, 2024). Implementation research is a critical frontier for AI edu- cation studies interested in impacting

Computer vision / image classification, 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.

6-8, 9-12, 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.
• Part of a university initiative supporting responsible AI for social empowerment and education, the project-based RAICA (Responsible AI for Computational Action) curricu- lum supports middle/high school learners and novice AI lit- eracy teachers use AI creativ

• 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.

Computer vision / image classification, Ethics / responsible AI

Not specified in extracted text

• Co-creator

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Ethics / responsible AI education.
• AI capability focus: Computer vision / image classification, Ethics / responsible AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• 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.

• Project-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: Project-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 offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, pro- vides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding ac- cess.

• This paper offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, pro- vides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding ac- cess.
• Twelve educators working with ~282 students across nine pilot sites in four countries used a bespoke fidelity of implementation data collection tool (pre-made comment prompts in a Google Docs version of the teacher guide) to provide 236 qualitative responses about AI literacy and re- sponsible desig

Part of a university initiative supporting responsible AI for social empowerment and education, the project-based RAICA (Responsible AI for Computational Action) curricu- lum supports middle/high school learners and novice AI lit- eracy teachers use AI creatively for good. This paper offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, pro- vides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding ac- cess.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Qualitative study

• 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
• Teacher professional development
• Ethics / responsible AI education
• Computer vision / image classification

• Completed

6-8, 9-12, Higher education

In-school (K-12), Higher education

Computer vision / image classification, Ethics / responsible AI

Project-based learning

Medium

Medium

Advancing Research on Equitable AI Education Through a Focus on Implementation: Insights from a Middle School Computer Vision Module Beta-Test

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

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

2025

10.1609/aaai.v39i28.35184

https://ojs.aaai.org/index.php/AAAI/article/view/35184

https://ojs.aaai.org/index.php/AAAI/article/view/35184/37339

021_Advancing Research on Equitable AI Education Through a Focus on Implementation_ Insights from a Middle School Computer Vision Module Beta-Te.pdf

8

Part of a university initiative supporting responsible AI for social empowerment and education, the project-based RAICA (Responsible AI for Computational Action) curricu- lum supports middle/high school learners and novice AI lit- eracy teachers use AI creatively for good. This paper offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, pro- vides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding ac- cess. We present a novel approach to analyzing fidelity of implementation data from RAICA’s computer vision module beta-test. Twelve educators working with ~282 students across nine pilot sites in four countries used a bespoke fidelity of implementation data collection tool (pre-made comment prompts in a Google Docs version of the teacher guide) to provide 236 qualitative responses about AI literacy and re- sponsible design activities, plus 111 ordinal ratings of em- bedded teacher supports. Analyses revealed that while the curriculum was generally implemented as designed, educa- tors frequently made modifications. Although most changes produced practical insights for improved curriculum design, others helped the design team anticipate and prevent changes that could obscure learning objectives and hinder outcomes. We discuss the pedagogical, design, and research implica- tions of these findings for effective AI teaching/learning in diverse settings.

• In-school (K-12)
• Higher education

• 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.