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The Essentials of AI for Life and Society: A Full-Scale AI Literacy Course Accessible to All

Zifan Xu, Kristen Procko, Michael Munje, Kristin Patterson, Lea Sabatini, Joydeep Biswas, Peter Stone

TL;DR

The paper documents transforming an informal, one-credit AI literacy seminar into a three-credit undergraduate course (CS 309) using a flipped-classroom model, asynchronous modules, Perusall-based readings, and integrated ethics, complemented by five non-programming assignments and a final ethics project. It demonstrates that the cohesive structure, non-programming emphasis, and ethics focus yield strong student engagement and positive feedback, addressing prior shortcomings of the initial offering. The authors provide a replicable curriculum and openly accessible materials to enable broader adoption of AI literacy at scale across disciplines. The work highlights the practical viability of accessible, interdisciplinary AI education that does not require programming prerequisites.

Abstract

In Fall 2023, we introduced a new AI Literacy class called The Essentials of AI for Life and Society (CS 109), a one-credit, seminar course consisting mainly of guest lectures, which was open to the entire university, including students, staff, and faculty. Building on its success and popularity, this paper describes our significant expansion of the course into a full-scale three-credit undergraduate course (CS 309), with an expanded emphasis on student engagement, interactivity, and ethics-related components. To knit together content from the guest lecturers, we implemented a flipped classroom. This model used weekly asynchronous learning modules--integrating pre-recorded expert lectures, collaborative readings, and ethical reflections--which were then unified by the course instructor during a live, interactive discussion session. To maintain the broad accessibility of the material (no prerequisites), the course introduced substantive, non-programming homework assignments in which students applied AI concepts to grounded, real-world problems. This work culminated in a final project analyzing the ethical and societal implications of a chosen AI tool. The redesigned course received overwhelmingly positive student feedback, highlighting its interactivity, coherence, and accessible and engaging assignments. This paper details the course's evolution, its pedagogical structure, and the lessons learned in developing a core AI literacy course. All course materials are freely available for others to use and build upon.

The Essentials of AI for Life and Society: A Full-Scale AI Literacy Course Accessible to All

TL;DR

The paper documents transforming an informal, one-credit AI literacy seminar into a three-credit undergraduate course (CS 309) using a flipped-classroom model, asynchronous modules, Perusall-based readings, and integrated ethics, complemented by five non-programming assignments and a final ethics project. It demonstrates that the cohesive structure, non-programming emphasis, and ethics focus yield strong student engagement and positive feedback, addressing prior shortcomings of the initial offering. The authors provide a replicable curriculum and openly accessible materials to enable broader adoption of AI literacy at scale across disciplines. The work highlights the practical viability of accessible, interdisciplinary AI education that does not require programming prerequisites.

Abstract

In Fall 2023, we introduced a new AI Literacy class called The Essentials of AI for Life and Society (CS 109), a one-credit, seminar course consisting mainly of guest lectures, which was open to the entire university, including students, staff, and faculty. Building on its success and popularity, this paper describes our significant expansion of the course into a full-scale three-credit undergraduate course (CS 309), with an expanded emphasis on student engagement, interactivity, and ethics-related components. To knit together content from the guest lecturers, we implemented a flipped classroom. This model used weekly asynchronous learning modules--integrating pre-recorded expert lectures, collaborative readings, and ethical reflections--which were then unified by the course instructor during a live, interactive discussion session. To maintain the broad accessibility of the material (no prerequisites), the course introduced substantive, non-programming homework assignments in which students applied AI concepts to grounded, real-world problems. This work culminated in a final project analyzing the ethical and societal implications of a chosen AI tool. The redesigned course received overwhelmingly positive student feedback, highlighting its interactivity, coherence, and accessible and engaging assignments. This paper details the course's evolution, its pedagogical structure, and the lessons learned in developing a core AI literacy course. All course materials are freely available for others to use and build upon.

Paper Structure

This paper contains 29 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. AI Literacy
  4. AI in University Curricula
  5. Non-Programming AI Assignments
  6. Course Design
  7. Curricular Objectives
  8. Course Structure
  9. Assignments and Projects
  10. Introduction to Algorithms
  11. Planning and Search
  12. Probabilistic Reasoning
  13. Machine Learning
  14. Generative AI
  15. Ethics Project
  16. ...and 14 more sections

Figures (3)

  • Figure 1: An overview of the course structure.
  • Figure 2: An overview of the non-programming tools used by five assignments.
  • Figure 3: Course Polls comparing CS 109 and CS 309. Bars show the percentage of respondents for each Likert option (per course). Panel legends display per-course mean and standard deviation.