Fostering Epistemic Insights into AI Ethics through a Constructionist Pedagogy: An Interdisciplinary Approach to AI Literacy

Source publication / research team or educational organization described in paper

China, Hong Kong

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

• In-school (K-12)

Classroom, course, or resource-based AI education activity

Not specified in extracted text

Not specified in extracted text

Generative AI, Ethics / responsible AI

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-5

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.
• There is a growing consensus on the importance of AI ethics in K-12 education, yet effective teaching remains a challenge.

• 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.

Generative AI, Ethics / responsible AI

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Teacher professional development, Ethics / responsible AI education.
• AI capability focus: Generative AI, Ethics / responsible AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• 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.

• 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.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published empirical study.
• Pedagogical pattern: 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 requires an interdisciplinary understanding of com- puter science, philosophy, and the humanities, alongside ep- istemic insights into how AI systems acquire, process, and apply knowledge differently from humans.

• AI ethics requires an interdisciplinary understanding of com- puter science, philosophy, and the humanities, alongside ep- istemic insights into how AI systems acquire, process, and apply knowledge differently from humans.

There is a growing consensus on the importance of AI ethics in K-12 education, yet effective teaching remains a challenge. AI ethics requires an interdisciplinary understanding of com- puter science, philosophy, and the humanities, alongside ep- istemic insights into how AI systems acquire, process, and apply knowledge differently from humans.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Pre/post or experimental 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
• Teacher professional development
• Ethics / responsible AI education
• Generative AI
• Ethics / responsible AI

• Completed

K-5

In-school (K-12)

Generative AI, Ethics / responsible AI

Hands-on / experiential learning

Medium

Medium

Fostering Epistemic Insights into AI Ethics through a Constructionist Pedagogy: An Interdisciplinary Approach to AI Literacy

• Ziyan Lin
• Yun Dai

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

2025

10.1609/aaai.v39i28.35190

https://ojs.aaai.org/index.php/AAAI/article/view/35190

https://ojs.aaai.org/index.php/AAAI/article/view/35190/37345

027_Fostering Epistemic Insights into AI Ethics through a Constructionist Pedagogy.pdf

7

There is a growing consensus on the importance of AI ethics in K-12 education, yet effective teaching remains a challenge. AI ethics requires an interdisciplinary understanding of com- puter science, philosophy, and the humanities, alongside ep- istemic insights into how AI systems acquire, process, and apply knowledge differently from humans. To address this challenge, this study presents the design, development, and implementation of three theory-informed activities aimed at fostering epistemic insight and ethical understanding of AI among upper primary school students (ages 10-12). Grounded in constructionism, our pedagogical design lever- ages hands-on experimentation with guided reflection to con- cretize complex AI concepts. Students examine rule-based, data-driven, and generative AI systems, employing mathe- matical reasoning to represent AI decision-making processes and reflect on ethical issues such as fairness, bias, and trans- parency. The interdisciplinary, constructionist approach en- courages learners to discern how AI knowledge construction differs from human cognition, thereby enhancing their ethical reasoning. The findings show that students not only devel- oped a foundational understanding of ethical principles but also gained epistemic insight into AI’s relationship with hu- man knowledge and values. This article provides a practical, theory-informed framework and interdisciplinary teaching resources to advance K-12 AI ethics education and support educators in fostering AI literacy.

• In-school (K-12)

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• 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

• group_size
• 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.