We approach this work through socio-material theories of literacy (Brandt, 1998; Mills, 2015; Orlikowski, 2007; Sørensen, 2001) to collectively inform how we understand literacy and technology in the context of youth writing with generative AI. A socio-material perspective of literacy broadens epistemological and methodological stances for studying literacy interactions with technology. Here, human experience and literacy practice are constituted through material relations (Sørensen, 2001), with physical materials acting as integral components of meaning-making (Burnett et al., 2014). In the current era, technology pervades homes and pockets through infrastructural systems (cables, satellites, microchips) and proprietary operating systems with its internal logic (search engine algorithms, cloud services architecture). These systems are invisible, embedded within the user-oriented interfaces (apps such as Google Chrome, WhatsApp) (Van Dijck, 2021). A socio-material theory of literacy challenges the assumption that technology, language, and communication function as a neutral conduit for meaning without distortion, mediation, or bias (Burnett et al., 2014). Through this perspective, we can explore how the youths’ social practices and technological materials shape prompting practices as well as the perpetuation biases, often evidenced in technological tools (Dixon-Román et al., 2020; Rosa &Flores, 2017)
This study is situated in a three-year, design-based research study for social change.
Design-based research for social change is an iterative methodological approach used in education research with an explicit focus on incorporating different forms of knowledge and expertise to design, implement, and study learning activities in the pursuit of equity. (Gutiérrez et al., 2020). We chose this methodology to include contextual factors and emphasize reorganizing GenAI systems rather than “fixing” individuals—a move that helps to challenge deficit and reductive notions of learners.
Primary data sources were collected each session of the gamers club via ethnographic methods (e.g., field notes, audio/video recording, informal interviews). The participants in this study are the fourteen youth of color (Ages 9-11) across two iterations (2023-2024 and 2024-2025) who volunteered to attend the club for the school year. All participants completed IRB permission.
We analyzed data using a reflexive thematic (Braun & Clarke, 2020) approach. We began identifying instances in which the youth participants prompted generative AI while creating their designs. We coded the literacy practices that led to the prompting and uptake of generative AI outputs. From these instances, we determined patterns across the participants, and these patterns constitute the preliminary findings.
Preliminary findings indicate that youth were not passive consumers of generative AI’s output as they composed their video games. Their prompting strategies demonstrated an assertion of authorship over their final composed products as they brought their interests, experiences, racial identities, languages, and funds of knowledge to the writing task. Yet, generative AI output did have influence within their processes of composing, positioning the youth to revise at the word and image level.
As recent federal legislation (Exec Order, 2025) promotes the advancement of AI within education, it is not a matter of if but when and how generative AI will be used in literacy instruction. It is of the utmost urgency to critically and intentionally examine how prompt engineering will be included in classrooms to plan for just futures. Fortunately, the time for incorporating youth’s insights on/with generative AI is now, as systems are currently still tunable. The significance of this study is that it shares findings centered on the youth’s prompting strategies on/with these platforms as embedded within a composing task. These insights are necessary for informing a more responsive version of equity at the curricular level.
Brandt, D. (1998). Sponsors of literacy. College Composition and Communication, 49(2), 165–185.
Braun, V., & Clarke, V. (2020). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Burnett, C., Merchant, G., Pahl, K., & Rowsell, J. (2014). The (im)materiality of literacy: The significance of subjectivity to new literacies research. Discourse: Studies in the Cultural Politics of Education.
Dixon-Román, E., Nichols, T. P., & Nyame-Mensah, A. (2020). The racializing forces of/in AI educational technologies. Learning, Media and Technology.
Gutiérrez, K. D., Susan Jurow, A., & Vakil, S. (2020). Social Design-Based Experiments. In N. S. Nasir, C. D. Lee, R. Pea, & M. McKinney De Royston (Eds.), Handbook of the Cultural Foundations of Learning (1st ed., pp. 330–347). Routledge. https://doi.org/10.4324/9780203774977-23
Mills, K. (2015). Literacy theories for the digital age: Social, critical, multimodal, spatial, material and sensory lenses. Multilingual Matters.
Orlikowski, W. J. (2007). Sociomaterial Practices: Exploring Technology at Work. Organization Studies
Sørensen, E. (2001). The Materiality of Learning: Technology and Knowledge in Educational Practice (1st ed.). Cambridge University Press.
Rosa, J., & Flores, N. (2017). Unsettling race and language: Toward a raciolinguistic perspective. Language in Society.
The White House. (2025, April 23). Advancing artificial intelligence education for american youth. Executive Order
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