The Essentials of AI for Life and Society: An AI Literacy Course for the University Community
Project Overview
The document outlines the implementation of a one-credit AI literacy course at The University of Texas at Austin, aimed at a diverse audience that includes students, faculty, and community members. The primary goal of the course is to improve understanding of AI fundamentals, ethical considerations, and its societal impacts. Participant feedback has revealed that while there are notable improvements in AI literacy, there are challenges related to course design and audience engagement. To address these issues and enhance interactivity, plans are being made to expand the course to three credits in the upcoming semester. This initiative reflects a broader trend in education to incorporate generative AI into curricula, emphasizing the importance of equipping individuals with the knowledge and skills necessary to navigate the evolving landscape of artificial intelligence. Overall, the course serves as a key application of generative AI in education, highlighting its potential to foster critical understanding and ethical awareness among a wide audience.
Key Applications
AI Literacy Course
Context: Targeted towards university students, faculty, staff, and community members to improve AI literacy.
Implementation: A 14-week seminar-style course delivered online with lectures from an interdisciplinary team, recorded for later access.
Outcomes: Participants reported gains in AI literacy, with improved understanding of AI's capabilities, limitations, and societal implications.
Challenges: Difficulty in catering to a diverse audience with varying levels of technical background and ensuring engagement among auditors.
Implementation Barriers
Engagement and Content Barrier
Auditors reported lower engagement compared to students, with some having different expectations for the course. Additionally, some course readings were deemed too challenging for a non-technical audience, leading to confusion.
Proposed Solutions: Increased interactivity and tailored content for non-technical audiences in future iterations. Revising reading materials to include more introductory texts and popular articles.
Project Team
Joydeep Biswas
Researcher
Don Fussell
Researcher
Peter Stone
Researcher
Kristin Patterson
Researcher
Kristen Procko
Researcher
Lea Sabatini
Researcher
Zifan Xu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Joydeep Biswas, Don Fussell, Peter Stone, Kristin Patterson, Kristen Procko, Lea Sabatini, Zifan Xu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai