Autonomous Agents: An Advanced Course on AI Integration and Deployment

Source publication / research team or educational organization described in paper

USA

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

• Higher education

Course implementation or course design

3 week grow peri- ods that we had allotted; 2 weeks

Not specified in extracted text

Robotics / physical AI, Assessment / tutoring analytics

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

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.
• A majority of the courses on autonomous systems focus on robotics, despite the growing use of autonomous agents in a wide spectrum of applications, from smart homes to intelli- gent traffic control.

• 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.

Robotics / physical AI, Assessment / tutoring analytics

Not specified in extracted text

• Tutor
• Automation tool

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Assessment support, Physical AI / robotics learning.
• AI capability focus: Robotics / physical AI, Assessment / tutoring analytics.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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.

• Tutoring / feedback-supported 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: Tutoring / feedback-supported 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.
• Fi- nally, we present some student feedback about the course and opportunities for future improvement.

• Fi- nally, we present some student feedback about the course and opportunities for future improvement.

A majority of the courses on autonomous systems focus on robotics, despite the growing use of autonomous agents in a wide spectrum of applications, from smart homes to intelli- gent traffic control. Our goal in designing a new senior-level undergraduate course is to teach the integration of a variety of AI techniques in uncertain environments, without the de- pendence on topics such as robotic control and localization.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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.

• Assessment / feedback quality
• Curriculum / course design
• Learning tool / resource design
• Assessment support
• Physical AI / robotics learning
• Robotics / physical AI
• Assessment / tutoring analytics

• Completed

Higher education

Higher education

Robotics / physical AI, Assessment / tutoring analytics

Tutoring / feedback-supported learning

Low to Medium

Medium

Autonomous Agents: An Advanced Course on AI Integration and Deployment

• Stephanie Rosenthal
• Reid Simmons

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

2023

10.1609/aaai.v37i13.26881

https://ojs.aaai.org/index.php/AAAI/article/view/26881

https://ojs.aaai.org/index.php/AAAI/article/view/26881/26653

072_Autonomous Agents_ An Advanced Course on AI Integration and Deployment.pdf

8

A majority of the courses on autonomous systems focus on robotics, despite the growing use of autonomous agents in a wide spectrum of applications, from smart homes to intelli- gent traffic control. Our goal in designing a new senior-level undergraduate course is to teach the integration of a variety of AI techniques in uncertain environments, without the de- pendence on topics such as robotic control and localization. We chose the application of an autonomous greenhouse to frame our discussions and our student projects because of the greenhouse’s self-contained nature and objective metrics for successfully growing plants. We detail our curriculum design, including lecture topics and assignments, and our iterative process for updating the course over the last four years. Fi- nally, we present some student feedback about the course and opportunities for future improvement.

• Higher education

• 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.