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

TL;DR

This work documents the rapid development of a university-wide AI literacy course aimed at non-technical audiences, addressing the gap between technical AI literature and public understanding. It describes a 14-lecture, 1-credit online format with interdisciplinary speakers, implemented with institutional support and iterative feedback to assess and improve AI literacy. Retrospective survey data indicate significant gains in participants’ AI literacy, though readings posed challenges for non-technical learners, guiding a planned shift to a 3-credit version with more approachable readings and asynchronous modules. The study provides a practical blueprint for delivering accessible AI education to broad university communities, including design decisions, evaluation strategies, and plans for scalable expansion across disciplines and audiences.

Abstract

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response 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 disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative 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.

Figures (2)

• Figure 1: Summary of survey responses on engagement and interest, difficulty of course materials, and overall understanding. The blue and orange bars represent responses from students and auditors, respectively. Subtitles indicate the prompts corresponding to each survey question.
• Figure 2: Retrospective pre-/post-survey questions. Each result is statistically significant, with a p-value $<$ 0.01, n = 151 for all questions, except for Q5 where n = 150.