This study focuses on a generative AI approach to facilitate qualitative analysis in Writing Studies research. We gathered 13,336 one-sentence to one-paragraph responses written by 3,334 incoming students in a directed self-placement program administered at a large R1 U.S. university. In these responses, students describe their high school writing experience and college writing expectations. In stage one of the project, we pilot the use of Retrieval-Augmented Generation to expedite the selection of relevant responses for a topic—in this case, students’ positive self-assessments as writers. The selected responses were then compared to a random sample and rated by three faculty with writing expertise. In stage two, these faculty generated codes and themes from a subset of the responses, incorporating ChatGPT-4 through the stages of thematic analysis. Results show that the use of AI expedites and enhances qualitative analysis, but human participation in the process is still essential. We suggest a machine-in-the-loop framework with which Writing Studies researchers can more readily integrate generative AI to study large corpora of student writing.

This article reports on a multiphase study designed to understand how nonexpert users interact with COVID-19 data dashboards, particularly in terms of the dashboards’ actionability, or ability to support decision making. Analysis of the videos and transcriptions of user interviews shows the variable relevance of proposed criteria for dashboard actionability and suggests additional criteria for users’ emotional responses to data and for the presentation of data at degrees of personal and local granularity. These findings advance an understanding of how nonexpert audiences interact with and derive value from complex visualized data.

How can we—as citizens and consumers, as teachers and students—develop the ability to understand, explore, and analyze data of various kinds in order to inform our decisions on matters that are important to us? The Dear Data project described in this webtext suggests that asking students to produce and visualize small personal data can open a process of engaging with data analytically and creatively.

InterviewsDaniel Anderson interviewed by Erin AndersonSusan Delagrange interviewed by Madeleine SorapureKeith Dorwick interviewed by Susan DelagrangeErin Anderson interviewed by M. Remi YergeauThomas Rickert & Michael Salvo interviewed by David RiederDavid Rieder interviewed by Thomas Rickert & Michael SalvoMadeleine Sorapure interviewed by Daniel AndersonVictor Vitanza interviewed by David RiederAnne Wysocki interviewed by Victor VitanzaM. Remi Yergeau interviewed by Anne Wysocki