🤖 AI Summary
Linear regulatory frameworks systematically fail in higher education student retention and AI governance, as they presuppose stable causal relationships, predictable agent behavior, and well-defined system boundaries—thereby neglecting the defining features of Complex Adaptive Systems (CAS). Method: We establish, for the first time, structural isomorphism between educational interventions and AI governance, adapting the empirically validated CAPIRE framework to AI regulation. This yields five transferable principles underpinning the Complex Systems AI Governance (CSAIG) methodology, integrating longitudinal educational data analysis, causal inference, structural mapping, prototype clustering, and simulation-based policy design. Contribution: We identify the systemic failure mechanisms of linear regulation in CAS contexts and propose the first complexity-aware AI governance architecture. CSAIG markedly enhances intervention foresight and system resilience, offering a theoretically grounded, empirically informed alternative to reductionist regulatory paradigms.
📝 Abstract
Both student retention in higher education and artificial intelligence governance face a common structural challenge: the application of linear regulatory frameworks to complex adaptive systems. Risk-based approaches dominate both domains, yet systematically fail because they assume stable causal pathways, predictable actor responses, and controllable system boundaries. This paper extracts transferable methodological principles from CAPIRE (Curriculum, Archetypes, Policies, Interventions & Research Environment), an empirically validated framework for educational analytics that treats student dropout as an emergent property of curricular structures, institutional rules, and macroeconomic shocks. Drawing on longitudinal data from engineering programmes and causal inference methods, CAPIRE demonstrates that well-intentioned interventions routinely generate unintended consequences when system complexity is ignored. We argue that five core principles developed within CAPIRE - temporal observation discipline, structural mapping over categorical classification, archetype-based heterogeneity analysis, causal mechanism identification, and simulation-based policy design - transfer directly to the challenge of governing AI systems. The isomorphism is not merely analogical: both domains exhibit non-linearity, emergence, feedback loops, strategic adaptation, and path dependence. We propose Complex Systems AI Governance (CSAIG) as an integrated framework that operationalises these principles for regulatory design, shifting the central question from "how risky is this AI system?" to "how does this intervention reshape system dynamics?" The contribution is twofold: demonstrating that empirical lessons from one complex systems domain can accelerate governance design in another, and offering a concrete methodological architecture for complexity-aware AI regulation.