An Analysis of Engineering Students’ Responses to an AI Ethics Scenario

Source publication / research team or educational organization described in paper

Canada

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

• Higher education

Classroom, course, or resource-based AI education activity

Not specified in extracted text

Not specified in extracted text

Ethics / responsible AI

• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

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.
• In light of significant issues in the technology industry, such as algorithms that worsen racial biases, the spread of online misinformation, and the expansion of mass surveillance, it is increasingly important to teach the ethics and sociotechni- cal implicat

• 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

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: 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.

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

• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

• Case classified under: Published empirical study.
• Pedagogical pattern: 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.
• We engage with prior research on pedagogical approaches to and considerations for teaching AI ethics and highlight some of the obstacles that engineering undergraduate students experi- ence in learning and applying AI ethics concepts.

• We engage with prior research on pedagogical approaches to and considerations for teaching AI ethics and highlight some of the obstacles that engineering undergraduate students experi- ence in learning and applying AI ethics concepts.

In light of significant issues in the technology industry, such as algorithms that worsen racial biases, the spread of online misinformation, and the expansion of mass surveillance, it is increasingly important to teach the ethics and sociotechni- cal implications of developing and using artificial intelligence (AI). Using 53 survey responses from engineering undergrad- uates, this paper measures students’ abilities to identify, mit- igate, and reflect on a hypothetical AI ethics scenario.

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

• Survey

• 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
• Ethics and responsible use
• Ethics / responsible AI education
• Ethics / responsible AI

• Completed

Higher education

Higher education

Ethics / responsible AI

Scenario / case-based learning

High

Medium

An Analysis of Engineering Students’ Responses to an AI Ethics Scenario

• Alexi Orchard
• David Radke

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

2023

10.1609/aaai.v37i13.26880

https://ojs.aaai.org/index.php/AAAI/article/view/26880

https://ojs.aaai.org/index.php/AAAI/article/view/26880/26652

071_An Analysis of Engineering Students#U2019 Responses to an AI Ethics Scenario.pdf

9

In light of significant issues in the technology industry, such as algorithms that worsen racial biases, the spread of online misinformation, and the expansion of mass surveillance, it is increasingly important to teach the ethics and sociotechni- cal implications of developing and using artificial intelligence (AI). Using 53 survey responses from engineering undergrad- uates, this paper measures students’ abilities to identify, mit- igate, and reflect on a hypothetical AI ethics scenario. We engage with prior research on pedagogical approaches to and considerations for teaching AI ethics and highlight some of the obstacles that engineering undergraduate students experi- ence in learning and applying AI ethics concepts.

• Higher education

• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

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.