White Paper 7: Solving Assessment in the Post-AI School: From Outcomes to Meshwork

This white paper proposes a fundamental redesign of assessment for AI-mediated learning environments. It argues that outcome-based models can no longer represent learning reliably, as artificial intelligence has decoupled performance from cognitive effort. Drawing on cognitive architecture, metacognition, and Tim Ingold’s concept of meshwork, the paper reframes learning as a dynamic, relational process that unfolds across time, context, and human–AI interaction. Through the Entagogy framework, it outlines how assessment can shift from evaluating static outputs to interpreting patterns of regulation, decision-making, and adaptation. The paper provides a strategic pathway for schools to move beyond performance proxies towards more truthful, process-rich models of learning and judgement.