Technical Communication Quarterly

This article explores the instructional design practices of three female artificial intelligence tech influencers (AITIs) who use YouTube to teach coding and professional literacies. Our thematic analysis of nine video transcripts featuring generative AI (GAI) yielded themes centered on influencers’ persona development, flexible instructional methods, opportunities for application, and GAI limitations. We conclude the article by identifying a utilitarian ethical dilemma wherein AITIs avoid overt feminist arguments to reach a broader audience.

A surge in public availability of emerging GenAI-AD has brought back the promises of automated accessibility for people who cannot see or see well. This article tests those promises through a double-rendering method that asks GenAI-AD engines to describe a simple portrait of a person and then returns these generated texts into GenAI-AD engines for visualizations of what they earlier had described, revealing insights about GenAI efficacies, ethics, and biases.

In this paper, we investigate two approaches to building artificial neural network models to compare their effectiveness for accurately classifying rhetorical structures across multiple (non-binary) classes in small textual datasets. We find that the most accurate type of model can be designed by using a custom rhetorical feature list coupled with general-language word vector representations, which outperforms models with more computing-intensive architectures.

This Methodologies and Approaches piece argues artificially intelligent machine learning systems can be used to effectively advance justice-oriented research in technical and professional communication (TPC). Using a preexisting dataset investigating patient marginalization in pharmaceuticals policy discourse, we built and tested 49 machine learning systems designed to identify and track rhetorical features of interest. Three popular and one new approach to feature engineering (text quantification) were evaluated. The results indicate that these systems have great potential for use in TPC research.

Analogical reasoning has long been an important tool in the production of scientific knowledge, yet many scientists remain hesitant to fully endorse (or even admit) its use. As the teachers of scientific and technical writers, we have an opportunity and responsibility to teach them to use analogy without their writing becoming “overly inductive,” as Aristotle warned. To that end, I here offer an analysis of an example of the effective use of analogy in Rodney Brooks's “Intelligence Without Representation.” In this article, Brooks provides a model for incorporating these tools into an argument by building four of them into an enthymeme that clearly organizes his argument. This combination of inductive and deductive reasoning helped the article become a very influential piece of scholarship in artificial intelligence research, and it can help our students learn to use analogy in their own writing. Every one who effects persuasion through proof does in fact use either enthymemes or examples: there is no other way. (Aristotle, 1984b Aristotle. 1984b. The rhetoric and the poetics of Aristotle, Edited by: Roberts, W. R. and Bywater, I. New York: The Modern Library. [Google Scholar], p. 26)