Using Case Studies to Teach Responsible AI to Industry Practitioners

Source publication / research team or educational organization described in paper

USA

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

• Professional / adult learning

Workshop / professional learning activity

Not specified in extracted text

resenta- tion time and extending group discussion time. Third, al- though about 20 participants joined the first session, there was substantial attrition by the fourth session, possibly due to work-related demands t

Ethics / responsible AI, Assessment / tutoring analytics

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Adult / workforce

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.
• Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable–—maximizing the benefits of technology while mitigating its risks.

• 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

Not specified in extracted text

• Evaluator

• Primary interaction pattern inferred from publication: Assessment support, Ethics / responsible AI education.
• AI capability focus: Ethics / responsible AI, Assessment / tutoring analytics.

• 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, Scenario / case-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Hands-on / experiential learning, Scenario / case-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.
• In this paper, we present a stakeholder-first educational ap- proach using interactive case studies to foster organiza- tional and practitioner-level engagement and enhance learn- ing about RAI.

• In this paper, we present a stakeholder-first educational ap- proach using interactive case studies to foster organiza- tional and practitioner-level engagement and enhance learn- ing about RAI.
• Assessment results show that participants found the workshops engaging and re- ported an improved understanding of RAI principles, along with increased motivation to apply them in their work.

Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable–—maximizing the benefits of technology while mitigating its risks. Industry practitioners play a crucial role in achieving the objectives of RAI, yet there is a persistent a shortage of consolidated educational resources and effective methods for teaching RAI to practitioners.

• 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.

• Conceptual understanding
• Engagement / motivation
• Ethics and responsible use
• Assessment / feedback quality
• Assessment support
• Ethics / responsible AI education
• Ethics / responsible AI
• Assessment / tutoring analytics

• Completed

Adult / workforce

Professional / adult learning

Ethics / responsible AI, Assessment / tutoring analytics

Hands-on / experiential learning, Scenario / case-based learning

Medium

Low to Medium

Using Case Studies to Teach Responsible AI to Industry Practitioners

• Julia Stoyanovich
• Rodrigo Kreis de Paula
• Armanda Lewis
• Chloe Zheng

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

2025

10.1609/aaai.v39i28.35177

https://ojs.aaai.org/index.php/AAAI/article/view/35177

https://ojs.aaai.org/index.php/AAAI/article/view/35177/37332

014_Using Case Studies to Teach Responsible AI to Industry Practitioners.pdf

8

Responsible AI (RAI) encompasses the science and practice of ensuring that AI design, development, and use are socially sustainable–—maximizing the benefits of technology while mitigating its risks. Industry practitioners play a crucial role in achieving the objectives of RAI, yet there is a persistent a shortage of consolidated educational resources and effective methods for teaching RAI to practitioners. In this paper, we present a stakeholder-first educational ap- proach using interactive case studies to foster organiza- tional and practitioner-level engagement and enhance learn- ing about RAI. We detail our partnership with Meta, a global technology company, to co-develop and deliver RAI work- shops to a diverse company audience. Assessment results show that participants found the workshops engaging and re- ported an improved understanding of RAI principles, along with increased motivation to apply them in their work.

• Professional / adult learning

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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.