Responsible Robotics: A Socio-Ethical Addition to Robotics Courses

Source publication / research team or educational organization described in paper

USA

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

• Research / curriculum design context

Course implementation or course design

11 hours of instruction on ethical and social issues as part of an undergraduate education (ACM 1991; 60 hours of ethics instruction

Not specified in extracted text

Computer vision / image classification, Robotics / physical AI, Ethics / responsible AI

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

Unspecified / broad 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.
• We are witnessing a rapid increase in real-world autonomous robotic deployments in environments ranging from indoor homes and commercial establishments to large-scale urban areas, with applications ranging from domestic assistance to urban last-mile delivery.

• 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, Robotics / physical AI, Ethics / responsible AI

Not specified in extracted text

• Automation tool

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Ethics / responsible AI education, Physical AI / robotics learning.
• AI capability focus: Computer vision / image classification, Robotics / physical AI, Ethics / responsible AI.

• 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.

• Instructional / curriculum-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: Instructional / curriculum-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.
• The developers of these robots in- evitably have to make impactful design decisions to ensure commercially viability, but such decisions have serious real- world consequences.

• The developers of these robots in- evitably have to make impactful design decisions to ensure commercially viability, but such decisions have serious real- world consequences.

We are witnessing a rapid increase in real-world autonomous robotic deployments in environments ranging from indoor homes and commercial establishments to large-scale urban areas, with applications ranging from domestic assistance to urban last-mile delivery. The developers of these robots in- evitably have to make impactful design decisions to ensure commercially viability, but such decisions have serious real- world consequences.

• 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.

• 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
• Curriculum / course design
• Learning tool / resource design
• Ethics / responsible AI education
• Physical AI / robotics learning
• Computer vision / image classification

• Completed

Unspecified / broad education

Research / curriculum design context

Computer vision / image classification, Robotics / physical AI, Ethics / responsible AI

Instructional / curriculum-based learning

Medium

Medium

Responsible Robotics: A Socio-Ethical Addition to Robotics Courses

• Joshua Vekhter
• Joydeep Biswas

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

2023

10.1609/aaai.v37i13.26885

https://ojs.aaai.org/index.php/AAAI/article/view/26885

https://ojs.aaai.org/index.php/AAAI/article/view/26885/26657

076_Responsible Robotics_ A Socio-Ethical Addition to Robotics Courses.pdf

9

We are witnessing a rapid increase in real-world autonomous robotic deployments in environments ranging from indoor homes and commercial establishments to large-scale urban areas, with applications ranging from domestic assistance to urban last-mile delivery. The developers of these robots in- evitably have to make impactful design decisions to ensure commercially viability, but such decisions have serious real- world consequences. Unfortunately it is not uncommon for such projects to face intense bouts of social backlash, which can be attributed to a wide variety of causes, ranging from in- appropriate technical design choices to transgressions of so- cial norms and lack of community engagement. To better prepare students for the rigors of developing and deploying real-world robotics systems, we developed a Re- sponsible Robotics teaching module, intended to be included in upper-division and graduate level robotics courses. Our module is structured as a role playing exercise which aims to equip students with a framework for navigating the con- flicting goals of human actors which govern robots in the field. We report on instructor reflections and anonymous sur- vey responses from offering our responsible robotics module in both a graduate-level, and an upper-division undergradu- ate robotics course at UT Austin. The responses indicate that students gained a deeper understanding of the socio-technical factors of real-world robotics deployments than they might have using self-study methods, and the students proactively suggested that such modules should be more broadly included in CS courses.

• Research / curriculum design context

• 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

• group_size

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.