From Educational Analytics to AI Governance: Transferable Lessons from Complex Systems Interventions

TL;DR

This paper presents a complexity-aware framework for AI governance inspired by educational analytics, emphasizing systemic understanding over linear risk assessments to better manage AI systems' emergent behaviors.

Contribution

It introduces CSAIG, a novel framework applying complex systems principles from education to AI governance, enhancing regulatory approaches for adaptive, non-linear systems.

Findings

Empirical validation of CAPIRE in educational dropout analysis

Identification of five core principles transferable to AI governance

Proposal of a new complexity-aware regulatory framework for AI

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.