The Essentials of AI for Life and Society: An AI Literacy Course for the University Community

Source publication / research team or educational organization described in paper

Not specified in extracted text

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

• Higher education
• Informal learning

Course implementation or course design

one-hour lectures synchronously

ck of consensus on what AI literacy means (Ng et al. 2021b), particularly for K-12 students. Early attempts by Ng et al. (Ng et al. 2021a) define “AI Literacy” as consisting of four components: 1) know and under; al is- sues. To provide guidance on how to create AI literacy cur- ricula for K-12 students, Ng et al. (Ng et al. 2023) conducted a systematic review of the literature on AI literacy, produc- ing pedagogical mod

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.
• We describe the development of a one-credit course to pro- mote AI literacy at The University of Texas at Austin.

• 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.

Assessment / tutoring analytics

Not specified in extracted text

• Tutor

• Primary interaction pattern inferred from publication: Curriculum / course design, Assessment support, Outreach / informal learning.
• AI capability focus: 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.
• Satisfyingly, we found that attendees reported gains in their AI literacy.

• Satisfyingly, we found that attendees reported gains in their AI literacy.

We describe the development of a one-credit course to pro- mote AI literacy at The University of Texas at Austin. In re- sponse to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinfor- mation and employment.

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

• Qualitative study
• 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.

• AI literacy
• Assessment / feedback quality
• Curriculum / course design
• Assessment support
• Outreach / informal learning
• Assessment / tutoring analytics

• Completed

Higher education

Higher education, Informal learning

Assessment / tutoring analytics

Tutoring / feedback-supported learning

Low to Medium

Medium

The Essentials of AI for Life and Society: An AI Literacy Course for the University Community

• Joydeep Biswas
• Don Fussell
• Peter Stone
• Kristin Patterson
• Kristen Procko
• Lea Sabatini
• Zifan Xu

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

2025

10.1609/aaai.v39i28.35166

https://ojs.aaai.org/index.php/AAAI/article/view/35166

https://ojs.aaai.org/index.php/AAAI/article/view/35166/37321

003_The Essentials of AI for Life and Society_ An AI Literacy Course for the University Community.pdf

6

We describe the development of a one-credit course to pro- mote AI literacy at The University of Texas at Austin. In re- sponse to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinfor- mation and employment. University students, faculty, and staff, and even community members outside of the Univer- sity, were invited to enroll in this online offering: The Essen- tials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final sur- vey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quanti- tative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience.

• Higher education
• Informal 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

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.