A Task-Attributed, AI-Supported Educational Architecture for AI-Displaced Societies (Ages 6–19)

This record presents a paired working-paper framework addressing educational formation and authority readiness in societies experiencing large-scale AI-driven labour displacement.

The primary paper proposes a task-attributed, AI-supported educational architecture for ages 6–19, designed to shift education away from labour-market sorting and toward resilience, ethical judgment, collective contribution, and adaptability under uncertainty. Learning is organised around real, bounded tasks rather than individual competition, ranking, or early specialisation. Education is treated as critical social infrastructure, with explicit design guardrails to ensure failures are visible, early, and reversible.

The companion framework addresses authority-critical professions (e.g. medicine, law, engineering, aviation), where responsibility, ethical judgment, and staged autonomy cannot be automated or decentralised. It introduces principles for non-delegable moral authority, public accountability, and auditable progression without elite capture, including safeguards against the subordination of ethical judgment to technical optimisation.

Together, the documents form a coherent systems proposal intended for policy analysis, pilot design, and further research. The framework is offered as a bounded, testable architecture rather than a prescriptive reform mandate, and is explicitly designed to operate under uncertainty, partial adoption, and real institutional constraints.

The work builds on and extends prior research into post-labour participation systems, including the Engagement Credit Economy (ECE), and is grounded in ongoing educational practice.

Extreme constraint environments — including remote terrestrial installations (e.g. polar stations, offshore platforms, disaster zones) and speculative off-world contexts — are treated here as stress-test cases rather than predictive scenarios. Their inclusion serves to expose hidden assumptions about authority, redundancy, and human judgment under irreversibility, not to forecast specific deployment contexts.