Taking Responsibility for Meaning and Mattering: An Agential Realist Approach to Generative AI and Literacy

Abstract Questions and concerns about artificial intelligence (AI) technologies in education reached a fever pitch with the arrival of publicly accessible, user‐facing generative AI systems, especially ChatGPT. Many of these issues will require regulation and collective action to address. But when it comes to generative AI and literacy, we argue that posthuman perspectives can help literacy scholars and practitioners reframe some concerns into questions that open new areas of inquiry. Agential realism in particular offers a useful perspective for exploring how generative AI matters in literacy practices, not as a unilaterally destructive force, but as a set of phenomena that intra‐actively reconfigures literacy practices. As a sociocultural (and as we argue, sociotechnical) practice, literacy arises out of the entanglement of bodies, spaces, contexts, positions, histories, and technologies. Generative AI is another in a long line of technologies that reconfigures literacy practices. In this article, we briefly explain how generative AI systems work, focusing on text‐based systems called Large Language Models (LLMs), and suggest ways that generative AI may reconfigure the sociocultural practice of literacy. We then offer three provocations to shift discussions about generative AI and literacy (1) from concerns about intentionality to questions of responsibility, (2) from concerns about authenticity to questions of mattering, and (3) from concerns about imitation to questions of multifarious communication. We conclude by encouraging literacy scholars and practitioners to draw inspiration from critical literacy efforts to discover what matters when it comes to generative AI and literacy.