AI Literacy: An Exercise in Power-Knowledge

Argues existing AI literacy frameworks, dominated by technical competency and responsible-use principles, enforce a consumer orientation toward AI rather than fostering genuine epistemic agency. Draws on Foucault's power-knowledge framework to propose a critical AI literacy that empowers learners to shape and challenge AI systems rather than merely use them.

Key Findings

The paper argues that dominant AI literacy frameworks — dominated by technical competency and responsible-use principles — enforce a "consumer" orientation toward AI rather than fostering genuine epistemic agency.

Drawing on Foucault's concept of power-knowledge, the authors contend that the absence of power as a construct in AI literacy discourse reflects a deeper conceptual failure: treating AI systems as neutral tools rather than as apparatuses that structure what can be known and by whom.

Grounded in Freire's pedagogy of critical consciousness and scholarship on digital literacy, the paper reconceptualizes 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.

Unequal access to AI tools, the authors argue, recapitulates longstanding epistemic injustices, so a literacy framework oriented toward empowerment must account for these structural inequities.

A three-part framework — contextual use, critical interrogation, and participatory governance — frames AI literacy as the cultivation of epistemic "agents" rather than the training of competent consumers of AI-generated information.

Theoretical Foundations

Generative AI has emerged not only as a new class of technologies but as an infrastructure for the creation and dissemination of knowledge, embedded in search engines, writing tools, research platforms, and industries including education. The authors argue that most institutional approaches reduce complex epistemic and ethical questions to technical proficiency: understanding how these models work, evaluating their outputs for accuracy, and using the tools "responsibly." Against this, the power-knowledge lens asks who can access and shape AI-mediated knowledge production, and how literacy frameworks either reproduce or challenge existing distributions of epistemic authority.

Implications for AI in Education

For education, the argument shifts the goal of AI literacy instruction from competent consumption toward critical participation. Curricula informed by this view would teach learners to interrogate the assumptions built into AI systems, understand how tool access and design encode power, and develop the capacity to shape and challenge the technologies that increasingly mediate learning. This connects AI Literacy to Equity: if unequal access recapitulates epistemic injustice, then AI literacy programs must attend to structural conditions, not only individual skills, and Critical Thinking becomes a political as well as cognitive capacity. The framework also challenges educators to treat learners as epistemic agents whose critical interrogation of AI is a legitimate and essential part of the curriculum, rather than a distraction from efficient use.

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Bias Mitigation

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Citation

Brady D. Lund, Zoë Abbie Teel (2026). AI Literacy: An Exercise in Power-Knowledge. arXiv:2607.27547. cs.AI, cs.CY.