Overview
Higher education faced an uncertain future following the public launch of ChatGPT in late 2022. Educators grappled with the relevance of their current instructional methods in this new era of generative AI. At the same time, articles touted the end of the college essay and the ability of AI to pass freshman year at Harvard. While conversations surrounding academic integrity were laden with concern, The University of Texas at Austin (UT) stepped in to set an empowering tone around the use of AI in education for its faculty. The University President named 2024 the “Year of AI“, and supported integration of artificial intelligence initiatives in research and education across campus.
One of the earliest initiatives in response to the ubiquitous generative AI landscape was a new course in the College of Natural Sciences (CNS) to promote AI literacy. Launched in the Fall of 2023, this high enrollment hybrid course was open to the entire University community (An AI Literacy Course for Anyone by Peter Stone).
A second initiative, developed in parallel with the AI literacy course, was the CNS AI in Education Grant Program, which encouraged innovative use of AI by CNS faculty. The resources found in this book are products of the projects supported by this grant program, which was administered through the Office of STEM Education Excellence (STEMx). The sections of the book reflect our retrospective grouping of the work into categories, while each chapter details an unique faculty-led project. Within each chapter, the reader will find a narrative from the author(s) about their experience executing their proposal and several instructor resources (e.g., activities, analyses, student feedback, example student work, syllabi, and more). Most chapters include a short video about the project. The videos were recorded at AI Live, a University-wide celebration culminating the Year of AI in November of 2024.
Part 1 of this book, Small Grants as Levers for Change, provides an overview of all projects and their administration. The first chapters describe the logistics of award administration while navigating evolving University policies regarding AI. At the culmination of these projects, the AI Live presentations began to reveal several shared themes. We explored these further through meetings and discussions, and present a summary of key themes and considerations in the chapter Project Outcomes and Major Themes. We particularly encourage instructors who are interested in developing AI projects to read this chapter.
Project Types
The remaining Parts of the book are organized into chapters that detail 16 projects representing nine disciplines in the College of Natural Sciences, grouped into four categories: 1) Integrating AI in Curriculum and Course Design, 2) Using AI to Step-Up Instructor Productivity, 3) Exploring AI in Experiential Learning, and 4) Building AI into Student Activities. To browse the projects within these categories in Pressbooks, the reader may use the “Contents” menu at the top of this page. To read through all chapters, note the navigation menus at the bottom. Below, we provide a summary of the categories with links to chapters.
1. Integrating AI in Curriculum and Course Design
This section contains three projects that involved significant integration of AI-related activities into the curriculum. An overview of materials and outcomes for an entire course focused on AI Literacy, developed by instructors in the computer science department, is described by Peter Stone in An AI Literacy Course for Anyone. The next chapter, An Introduction to AI in an Honor’s Course by Carlos Baiz, provides lecture materials designed to enhance student’s understanding of AI for a one-credit chemistry honor’s course. Despite the application in chemistry, we believe instructors will find these materials adaptable to many courses beyond the Natural Sciences. Similarly, Deborah Sztejnberg in Human Ecology describes an extensive integration of generative AI to support the creation of materials to seek professional opportunities. This Chapter, AI-Facilitated Job Application Materials, will also be applicable to individuals in many disciplines.
2. Using AI to Step-Up Instructor Productivity
This section of this book opens with a chapter on using AI to develop teaching lesson plans. In Creating 5E Teacher Lesson Plans with AI, Kelli Allen describes the application of generative AI tools in a teacher training course (the UTeach program), which is applicable to any instructor approaching lesson plan design. Next, in the chapter AI-Assisted Exam Creation, Jen Moon compares two generative AI tools for writing genetics exam questions based on her previous tests. In a complimentary activity, Sally Ragsdale in the Statistics and Data Sciences department describes the use of generative AI to create rubrics for grading assignments (AI to Co-Create Grading Rubrics). Concluding this section is a contribution from Ann Thijs in Biology that describes a method to target instruction to students’ learning needs in very large courses. Summarizing Student Feedback with AI details the categorization of hundreds of student comments where they described their reflection on challenging topics.
3. Exploring AI in Experiential Learning
A computer science-based project by Sarah Abraham and Paul Toprac opens this section of the book. Their chapter, AI in Video Game Development Research, involves the application of myriad AI tools in an effort to explore the effectiveness of AI as an assistant in video game creation. The following chapter, Testing AI Predictions of Protein Structure and Binding by Josh Beckham, describes a biochemistry-based research project activity applicable to research in drug discovery and development. Section 3 concludes with AI-Assisted Design of Polymerase Chain Reaction Primers by Katherine Bruner, where students determine whether AI can serve as an assistant for designing their laboratory experiment.
4. Building AI into Student Activities
The final section of the book includes six activities suitable for classroom-based settings. Although some of these were developed in the context of laboratory courses, they do not require a lab or research setting for their execution. The chapter entitled Term Project: AI Article Critiques and Infographics by Aprile Benner describes the use of generative AI in a Human Development and Family Sciences (HDFS) term project, either to evaluate its effectiveness in creating a project graphic or generating a critique of a popular article. Another contribution from faculty in HDFS is detailed by Sara Villanueva in AI-Assisted Article Review Assignment. This activity encourages students to use AI as an assistant for writing an article review and present a piece of hybrid work that highlights their contributions and the portions generated by AI. Layla Guyot in Statistics and Data Sciences tests the use of AI to augment student work as well. Noting that students were already using AI with varying levels of success in computer coding, Guyot incorporated an assignment that encourages students to generate their own code and critically analyze the output of AI tools in performing the task (AI-Supported Coding Assignments).
Several activities involved exploring the research literature in different ways. In Visualizing Primary Literature Search with AI by Kristen Procko, students compared traditional text-based search methods for literature search with a tool that generates a visual map of citations. A four part activity developed by Amanda Vines Garrett explores the utility of generative AI in biochemistry manuscript summary and interpretation (Reading Scientific Literature with AI). Thushani Herath describes a related activity, which was applied to an analytical chemistry project in AI-Assisted Scientific Literature Reviews. Students first performed a research activity in the usual way, and then used generative AI to replicate the assignment and compared their work to the AI output.
Aim of this Book
These resources may serve as a starting point for educators who would like to incorporate AI into their teaching practice. Instructors are welcome to utilize and remix the resources, adapting them to suit their unique classroom needs. We hope that our work inspires others to develop innovative resources and tools for teaching with AI, so that students may be prepared for tomorrow’s technology advances.
