Literacy and STEM Teachers Adapt AI Ethics Curriculum

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

• Higher education

Curriculum design or implementation

Not specified in extracted text

nificant efforts in developing and testing new curricula for AI education for K-12 students (Tourestzky et al. 2019), including elementary school students (Kim et al. 2021), middle school students (Zhang et al.; social studies classrooms (van Brummelen, and Lin, 2020). We collaborated with 4 teachers in STEM and literacy classrooms to learn similarities and differences in how they adapted our modules to fit their cont

Ethics / responsible AI

• Teacher readiness, time, support, and classroom integration may affect implementation quality.

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.
• This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts.

• 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

Language context discussed in source publication

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Outreach / informal learning, 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.

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

• Teacher readiness, time, support, and classroom integration may affect implementation quality.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Project-based learning, 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.
• AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools.

• AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools.

This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools.

• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Design / conceptual evidence

• 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
• Outreach / informal learning
• Ethics / responsible AI education

• Completed

Higher education

Higher education

Ethics / responsible AI

Project-based learning, Hands-on / experiential learning

Medium

Medium

Literacy and STEM Teachers Adapt AI Ethics Curriculum

• Benjamin Walsh
• Bridget Dalton
• Stacey Forsyth
• Tom Yeh

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

2023

10.1609/aaai.v37i13.26906

https://ojs.aaai.org/index.php/AAAI/article/view/26906

https://ojs.aaai.org/index.php/AAAI/article/view/26906/26678

097_Literacy and STEM Teachers Adapt AI Ethics Curriculum.pdf

8

This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler’s TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.

• Higher education

• Teacher readiness, time, support, and classroom integration may affect implementation quality.

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

• duration

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.