Workshop Information

We are pleased to offer the following workshops at the 2025 Computers and Writing Conference.  All workshops will be held from 12pm-4pm on May 15th, and will require a separate registration fee of $75.  To pay for the workshop, visit our Conference Payment Site.  

Podcasting and Other Audio Productions

Description: This hands-on workshop will cover the development, production, and publication of audio projects in and around rhetoric and writing studies.  Workshop led by Eric Detweiler (Middle Tennessee State University).

Text Analysis with R

Description: This workshop will feature an introduction to text analysis (sometimes called macroanalysis, Jockers 2013 or text analytics) with non-programming tools and with the R programming language. No prior experience is necessary. Participants in the workshop will also learn how to download, install, and run scripts on R Studio and how to use basic functions in R. An overview of related resources for working with text data with R will be provided, including different methods of analysis and data visualization. Methods include frequency analysis and analysis of ngrams with tidytext (Silge and Robinson 2021). Participants will also have the option to bring their own dataset (whether literary, in English, or another language or genre) to create a corpus. 

This session is open to all and no previous experience with R is necessary; it is based on regularly held workshops at the University of Georgia Research and Computational Data Management Department in the Libraries. Workshop led by Katie Ireland, Lab Coordinator for UGA's Department of Research and Computational Data Management in the UGA Libraries.
 

Reading With/out AI: A Working Group from UT Austin's Digital Writing and Research Lab

Description: Recent news about generative AI (e.g. ChatGPT) have concentrated our attention to generative writing and what such tools mean for the field and the teaching of writing. Little to no attention, however, has yet been given to the emerging technologies and techniques for reading and research. These developments make clear that while we are still reacting to the onslaught of AI erupting in our classrooms, yet another wave is on its way, and new reading practices have the potential to shift the landscape of our fields. How are we teaching our students to read? What might reading machines do to the work of composition, the labor of poring over texts, or, now, the labor of having texts pour over us? Will these tools bolster or erode students’ reading, writing, and learning?

Our workshop addresses how machine reading and writing technologies affect the work of composition. First, we'll give an overview of machine reading tools and practices, including Adobe’s recent incorporation of AI into its PDF reader (Acrobat) that turns PDFs into chatbots; emerging Google AI initiatives that a range of tools to collect, summarize, and analyze arguments and intertextuality amongst texts (NotebookLM); and tools that turn texts into NPR-style conversations (Illuminate). Alongside these tools, we will also preview a suite of research oriented applications (Research Rabbit, Connected Papers, LitMaps, Iris.Ai ). Next, the workshop leaders will guide participants through a structured individual work session where participants will be provided with a collection of texts to use as they experiment with these different machine reading tools. They will explore what possibilities–such as summary functions, text comparisons, and audio options–are made available through these different tools. The workshop will conclude with a large group discussion regarding the implications of these machine reading and research tools, as well as potential paths forward for the field of composition. Workshop led by a Working Group from the Digital Writing and Research Lab (University of Texas) -- Ali Gunnells, Sam Turner, Carlee Baker, and Maddie Bruegger.

Can we Teach Ethically with AI?

The pressure to “teach with AI” is increasing even for those of us trained in the humanities. While some of us embrace the opportunities, others of us resent the intrusion. In either case, there are a number of ethical issues that are often ignored in these discussions—ones that go beyond concerns of plagiarism or the theft of intellectual property. In particular, without grasping how some features of the underlying architecture of Large Language Models (LLMs) work, it’s very hard to grasp the magnitude of the biases that are baked into how they work. 

In this workshop session, we will work to establish a shared understanding of the range of AI applications, engage in an exercise that demonstrates this weakness of LLMs, and then share concerns and pursue an understanding of the range of ethical challenges that LLMs raise. Participants will leave with a deeper understanding of some of the inherent weaknesses of LLMs, along with ideas for how to structure these discussions with their students to help students develop the critical thinking skills necessary to engage productively with AI in the future. Workshop led by Kimberly Van Orman of the University of Georgia's Institute for Artificial Intelligence.