Composing with AI

Interfacing Chat GPT: A Heuristic Approach for Improving Generative AI Literacies

Desiree Dighton East Carolina University

Introduction

In “Politics of the Interface” (1994), Selfe and Selfe imagined a future in which writing technologies would be shaped by computers and writing research. As writing studies has developed alongside the evolution of personal computing, we've adapted our writing theory, practice, pedagogy, and research to social and technological transitions, maintaining a commitment to student agency. To support students in becoming effective writers in school, professional, and social contexts, we emphasize flexible, individualized processes like brainstorming, researching, drafting, integrating feedback, revising, citing source material, and more. We've spent decades convincing our students that the process they go through to generate a text is as valuable as and is inseparable from the end goal of their written products. Over the years, we've advocated for more inclusive writing and assessment practices and standards that take seriously student writing processes and their written products as academic writing with value. We've moved writing rubrics away from a "Standard" Written English and toward honoring students' home languages and linguistic diversity. As a field, we've largely embraced technologies as tools and avenues to expand the potentials for student agency in writing their personal and professional futures. Many of us have thoughtfully adapted our pedagogies and assignments to integrate technologies and student writing models, viewing these pivots as a necessary to help students fulfill professional and personal goals.

The Interface as a Site of Critical AI Literacies

Selfe and Selfe (1994) identified computer interfaces as “linguistic and cultural contact zones,” a borderland potentially harmful to non-dominant groups (p. 485). While these interfaces provide access to technology and enable user agency for problem solving (Grabill, 2003), interfaces have been designed as “paths of least resistance” (Stanfill, 2015) that guide users' access and shape their behaviors. Interfaces become frames we look through, losing their visibility and legibility to users (Jones, 2021; Selfe and Selfe, 1994; Stanfill, 2015). Rather than being neutral and transparent, interfaces circulate dominant “values of our culture—ideological, political, economic, educational” (p. 485). To “unconceal” (Selfe and Selfe, 1994, p. 501) the dominant values in the age of Generative AI (GAI) like ChatGPT, we can cultivate critical AI literacies through interface heuristic analysis. This situates interfaces in their material context, as the intersection of interacting elements (Jones, 2021, p. 1) and the “complex set of material relations among culture, technology, and technology users” (Selfe and Selfe, 1994, p. 485). This heuristic interface approach will constitute a growing field of research and practice of critical AI literacies—perhaps the next great turn for writing studies.

The focus on critical GAI literacies builds upon our field's established understanding of the integral relationship between technology, writing, and literacy (Gee, 2004; Hawisher and Selfe, 2000; Selber, 2004; Selfe, 1999; Yancy, 2003). GAI necessitates complex digital literacies that address “the nature, capacities, and risks of AI tools” (MLA-CCCC Joint Task Force on Writing and AI). How do we cultivate adaptable classroom practices for navigating evolving GAI like ChatGPT? We begin with GAI's materiality, questioning the affordances and constraints of ChatGPT's interface. How does it guide our interaction with the underlying Large Language Models (LLMs) and Natural Language Processes (NLPs)? While others in this collection offer recommendations for ChatGPT collaborations, this chapter offers heuristic interface analysis as one method for supporting critical AI literacies. The next section highlights theories and practices of interface design and analysis as the foundation for heuristic approaches to developing critical AI literacies with ChatGPT.

ChatGPT: A Conversational Interface

ChatGPT's widespread and rapid public proliferation hinged on its interface design, which shifted to real-time language input rather than pre-programmed features like tool bars, drop down menus, and various buttons. While chat-based interfaces are common (e.g., customer service bots) ChatGPT's human-like interactions are powered by vast data and advanced natural language technologies (See Figure 1 and Figure 2).

It's the human-like feel of generative AI's interface that has super-charged the “world-making power” (Jones, 2021) of its interface and propelled its circulation to rapidly reshape our educational and informational landscapes.