Re-Architecting Education, Authority, and Research for AI-Enabled Societies

Abstract

This record presents a versioned working-paper architecture addressing educational formation, authority readiness, tertiary education, and research governance in societies undergoing large-scale AI-driven transformation. Version 1 introduces a task-attributed, AI-supported educational architecture for ages 6–19, designed to shift education away from labour-market sorting, credential competition, and early specialisation toward resilience, ethical judgment, collective contribution, and adaptability under uncertainty. Learning is organised around real, bounded tasks rather than ranking or individualised optimisation. Education is treated as critical social infrastructure, with explicit design guardrails to ensure failures are visible, early, and reversible. Version 2 (Companion Framework) extends the architecture to 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. Version 3 further extends the framework to tertiary education, universities, and research systems. It argues that while AI dissolves traditional monopolies over knowledge transmission and credentialing, institutions remain necessary where human responsibility is non-delegable. The paper proposes a functional re-architecture of tertiary systems, distinguishing between depth convergence institutions, authority-critical research formation, and shared research infrastructure. It introduces a Research Readiness Framework (RRF) to operationalise authority formation in high-risk research domains and specifies governance, funding, oversight, and crisis-response mechanisms under AI-enabled conditions. Taken together, the documents form a coherent, layered systems architecture intended for policy analysis, pilot design, and further research. The framework is offered as a bounded, testable reference architecture, not 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 and value recognition systems, including the Engagement Credit Economy (ECE), which provides complementary economic foundations for contribution, stewardship, and responsibility outside wage-labour frameworks. The architecture is grounded in ongoing educational and policy practice. Extreme-constraint environments — including remote terrestrial installations (e.g. polar stations, offshore platforms, disaster zones) and speculative off-world contexts — are treated strictly as stress-test cases, not predictive scenarios. Their inclusion serves to surface hidden assumptions about authority, redundancy, and human judgment under irreversibility, rather than to forecast specific deployment contexts. This research is produced independently under the Drive-In s.r.o. research programme.Readers who wish to support its continuation may do so here: https://ko-fi.com/johnryder99892

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