Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named

Agentic Literacy Debt names a critical gap in the AI Literacy landscape that has become urgent with the rise of autonomous AI agents. Existing AI literacy frameworks assume humans evaluate AI outputs and then decide — they were built for a world of tools, not agents. But modern AI agents plan, decide, and act without step-by-step human approval, creating a structural deficit when deployed without corresponding literacy infrastructure. The debt compounds through three reinforcing channels: (1) normalization of opaque delegation, (2) multi-agent ecosystem complexity, and (3) institutional path dependence. Critically, the debt is incurred by deploying organizations but paid by users, patients, and citizens — a responsibility asymmetry that parallels arguments in GenAI Minoritized Knowledges Disability about who bears the costs of AI deployment. The paper reframes AI Literacy from an evaluative competency ("can you spot AI errors?") to a governance capability ("do you understand what you've delegated, and can you contest it?"). This connects to Digital Literacy Illusion findings that students overestimate their AI readiness, and to Over Reliance research showing that delegation without understanding produces dependency. Published in AI & Ethics, this paper has immediate implications for K 12 and Higher Ed AI literacy curricula, suggesting they must add agentic delegation concepts alongside traditional evaluation skills.

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Rohith Nama (2026). Agentic Literacy Debt: A Structural Problem the AI Literacy Field Has Not Yet Named. arXiv:2605.27396. AI & Ethics.