This series of posts is adapted from a keynote address given by Stuart A. Selber for the Teaching Technical Communication and Artificial Intelligence Symposium on March 20, 2024 hosted by Utah Valley University.
As we kick off the 2024 fall term, I want to offer something of a conceptual manifesto for how to think about artificial intelligence (AI) in the context of technical communication programs. I hope to provoke pedagogical and programmatic initiatives that are both productive for our students and responsible to our field.
The manifesto includes five tenets, each of which will be explored in its own post:
- AI is evolutionary, not revolutionary
- AI both solves and creates problems
- AI shifts, rather than saves, time and money
- AI provides mathematical solutions to human communication problems
- AI requires students to know more, not less, about technical communication
Teachers can use these tenets as talking points for their students and to frame curricular developments and revisions in their courses and programs.
Which leads me to our focus for this week.
Tenet # 1: AI is evolutionary, not revolutionary
When I was working on my dissertation in the early 1990s at Michigan Technological University, hypertext was all the rage, and many scholars in our field considered it to be a revolutionary technology that promised to suddenly change the nature of textuality in central ways. I am thinking especially of scholars in the groundbreaking collections edited by Edward Barrett (1988, 1989) and Paul Delany and George Landow (1991). But what hypertext really offered us was a platform for enacting postmodern theories of writing and reading that were at least two decades old. In this respect, hypertext was more evolutionary than revolutionary in nature.
Historian of science Michael Mahoney (1996) has argued quite convincingly that “Nothing is entirely new, especially in matters of scientific technology. Innovation is incremental, and what already exists determines to a large extent what can be created” (773). We see this reality in AI itself: How can an AI chatbot generate anything entirely new when its training data comes from the historical past?
Technical communication teachers and program directors have managed to domesticate everything from microcomputers and mobile devices to production and communication platforms to course-management systems and the internet of things. We will also learn how to domesticate AI for our purposes and contexts.
Historically, a popular approach to the curricular integration of technology has been to “forget technology and remember literacy,” to reference what my dissertation director Cynthia Selfe (1988) wrote in the late 1980s. What continues to be powerful about this sentiment is that it reminds us that what we already know about teaching and learning will go a long way toward helping us address artificial intelligence.
This is why the AI position statement from the Association for Writing Across the Curriculum re-affirms best practices grounded in decades of writing research. So too does the AI position statement from the MLA-CCCC Joint Task Force on Writing and AI.
There is a trap, however, and that is relying too heavily on one-way literacy models as a foundation for AI initiatives. Many people simply transfer their existing assumptions, goals, and practices into AI contexts. Although it is comfortable and sensible to begin with current ways of knowing and working, such an approach is ultimately limiting because it is non-dialogic: Not only does the model assume that AI is neutral, but it fails to recognize that AI can encourage us to reconsider taken-for-granted assumptions, goals, and practices.
So, in addition to addressing the possibilities and problems of AI, we should also see this liminal moment as an opportunity to revisit the status quo and consider how AI might encourage us to reinvent certain aspects of the field, including writing processes and the roles and responsibilities of technical communicators. On the broadest level, one of the more valuable aspects of AI might end up being that it can defamiliarize the familiar, as sociologist Zygmunt Bauman (2005) might put it, at least for the foreseeable future, so that we can look anew with fresh eyes at how we construct our professional world.
References
Barrett, Edward, ed. 1988. Text, Context, Hypertext: Writing with and for the Computer. Cambridge: MIT Press.
Barrett, Edward, ed. 1989. The Society of Text: Hypertext, Hypermedia, and the Social Construction of Information. Cambridge: MIT Press.
Bauman, Zygmunt. 2005. Liquid Life. Cambridge: Polity Press.
Delany, Paul, and George P. Landow, eds. 1991. Hypermedia and Literary Studies. Cambridge: MIT Press.
Mahoney, Michael S. 1996. “Issues in the History of Computing.” In History of Programming Languages, Volume 2, edited by Thomas J. Bergin and Richard G. Gibson, 772-81. Reading: Addison-Wesley Professional.
Selfe, Cynthia L. 1988. “The Humanization of Computers: Forget Technology, Remember Literacy.” The English Journal 77 (6): 69-71.
