A taxonomy of literacy practices for engaging with AI, grounded in the new literacies of online reading comprehension, is proposed, providing educators with language and observable markers for supporting students' evolving literacy practices.
Abstract
Generative artificial intelligence (AI) is transforming reading and writing practices in and out of educational contexts, yet few frameworks exist to support students' responsible engagement with these tools. This conceptual paper proposes a taxonomy of literacy practices for engaging with AI, grounded in the new literacies of online reading comprehension (Leu et al., 2004, 2015); Coiro, 2021). Using Leu et al.'s (2015) five processing practices and Coiro's (2021) multifaceted heuristic as an analytical lens, we identify seven interconnected practices students enact when reading and writing with AI. We conceptualize these not as hierarchical competencies but as socially situated practices enacted differently across contexts and purposes. For each practice, the taxonomy provides a description, an observable indicator, and ethical considerations embedded as intrinsic dimensions. This framework extends established new literacies scholarship into AI-mediated environments, providing educators with language and observable markers for supporting students' evolving literacy practices.
As generative artificial intelligence becomes one of the most significant systems of knowledge production in our society today, questions relating to who can access and shape that production grow increasingly important in our discourse. This paper argues that the existing frameworks for AI literacy, which are dominated by technical competency and responsible-use principles, are insufficient because they enforce a"consumer"orientation toward AI rather than fostering genuine epistemic agency. Based upon Foucault's concept of power-knowledge, Freire's pedagogy of critical consciousness, and scholarship of digital literacy, this paper proposes a reconceptualization of AI literacy as a critical practice that equips individuals not just to use AI systems, but to critically evaluate them, resist their structuring assumptions, and participate in their governance. The paper further argues that unequal access to AI tools in society recapitulates longstanding epistemic injustices, and that a literacy framework oriented toward empowerment must account for these structural inequities. A three-part framework of AI literacy based on the notions of contextual use, critical interrogation, and participatory governance frames this literacy as a cultivation of epistemic"agents"rather than the training of competent consumers of AI-generated information.
The Responsible AI Literacy in Education (RAIL-Ed) framework is introduced, developed through a systematic review and qualitative framework analysis of 67 studies (2023-2025), grounded in critical, pragmatist, sociocultural, and human-centered traditions.
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