Composing with AI

Conclusion: Integrating Text-to-Image Generative AI in the Classroom

Text-to-image generative AI can be more than a medium of composition or art—it can produce artifacts for analysis and discussion that support multiple learning outcomes of the composition classroom. Students can engage with the relationship between text and images through firsthand generation and analysis, reflecting on their own role in the prompting process while also analyzing how other actors influence the generated artifact. Students can analyze the message's pedagogies of sight and learn how the often-implicit visual elements of genres and representations can direct an audience's interpretation of images. This practice with and interrogation of AI additionally instructs a critical and reflective use of technologies. Taken together, these discussions and learning outcomes move the classroom toward instructing a critical multimodal composition practice.

To promote meaningful transferability of multimodal composition practices, composition instructors can pair discussions about the AI biases analyzed with classroom engagement of other theories on technology bias that have been developed in composition studies. Before discussing AI's situatedness and its pedagogy of sight, instructors might introduce how the internet and one's own search results are ideologically informed by introducing what Jeremy David Johnson terms "network bias" (Johnson). Network bias, as Johnson explains it, refers to the ways digital systems and search spaces are influenced by multiple layers of biases that include cultural preferences, geographical locations and limits, tracking technologies that pull from a user's identity and previous search practices when filtering and curating content for them, and networks that use crowdsourcing rather than information accuracy and relevance to determine the most important results. The composition class might reflect on network bias by using search engines and comparing the differences in their results. As Boylan explains, "When we ask Google to search for us, we have no way to control what is ‘found,' and no sense of who might have had influence over what images were chosen for viewing. To put it another way, we don't know who wanted us to find the images that do appear" (Boylan 76-77). Framing a search engine activity this way could help students see the very human nature of technology bias, encouraging them to interrogate how networks, AI, and other technologies are not creating bias themselves; rather, the bias is pulled from already existing structures. Comparing their results with one another would allow them to mobilize critical literacies and question how their identities, decisions, and digital tracking might influence their own search results.

Additionally, these practices can be connected to course discussions that center critical interrogation of the politics and ideologies underlying interfaces and the visual design of technologies (Selfe and Selfe; Bridgman et al.). Cynthia L. Selfe and Richard J. Selfe emphasize how teachers can aid students in recognizing the ideological underpinnings of everyday technologies, interrogating "the interface as an interested and partial map of our culture and as a linguistic contact zone that reveals power differentials" (495). And Bridgman, Fleckenstein, and Gage have outlined "rhetorical looking" as a framework that can be used to address how the normate body of the idealized user enacts violence on actual bodies through interface processes of speed, transparency, and forgetful seeing. Analyzing bias informed search results, examining ideological technology design, and rhetorical looking at interfaces can all reciprocally feed into discussions of AI images and their pedagogies of sight that similarly influence how audiences interpret and use visual rhetoric.

Engagement with text-to-image generative AI in the classroom might happen on a number of levels. Discussions and activities with concepts like network bias and rhetorical looking could extend into AI applications via major assignments or activities. Composition instructors could ask students to use these AI to produce images for visual or multimodal projects, employing critical and rhetorical literacy practices emphasized in the course to inform how they navigate the technology and to reflect on the images they produce with it. Or, text-to-image generative AI could support smaller scale in-class discussions and activities about visual bias and composing with technology. With a set of guiding questions like those in the bulleted list below, text-to-image generative AI can become a meaningful way to support existing concerns of the classroom like multimodality, visual rhetoric, composing with technology, rhetoric and genre, or critical and ethical research practices.

• What stereotypical representations are present? (gendered, cultural, social, economic, etc.)
• Do the images thematically depict a subject in a positive or negative light? How so?
• Describe any underrepresentation or overrepresentation.
• How do the images reinforce or challenge societal norms?
• Are there any gender, race, or age-related biases that you noticed? Explain.
• Describe the cultural perspective created for the viewer.
• How might the image affect different audience members?
• What biases might be present in the prompt's textual description? How do the images the AI creates reflect those biases?
• What trends do you notice across the outputs generated? What difficulties did you encounter when trying to get the images to look how you wanted?
• How might these biases reinforce power structures or stereotypes?
• What genre characteristics are present? How do they affect the image and how you might interpret it?
• What might be some ethical considerations about the sourcing of these generated artifacts?

Whether through large scale implementation or smaller scale discussion-based integration, text-to-image generative AI and bias in the composition classroom can help us move forward with AI in a productive way. These approaches pursue the possibility of composition classes that equip students with the practices necessary for them to engage with technologies of all sorts critically and ethically. This kind of movement not only provides students with responsible skills and practices for writing in a digital culture, but also prepares them to engage a professional world in which these technologies will forever be a moving target.