Nama et al. (2026) — Independent researcher. AI & Ethics.
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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