How Education Systems can Learn more Successfully: Orchestrating Intelligence at Scale

This paper examines the challenges and strategies for enhancing collective intelligence within education systems, focusing on how effectively these systems observe, assess, and utilise new knowledge, particularly through the lens of teachers' experiences. The COVID-19 pandemic and the introduction of AI tools like ChatGPT served as critical tests, revealing significant gaps in how education systems manage innovations and adapt to new knowledge. The paper sets out a framework for thinking about system level intelligence, including examples from around the world, and some of the specific challenges of learning how best to use AI. It then presents empirical analysis of teachers' experiences of using new knowledge. Interviews with teachers from high-performing schools in Chile highlight a persistent reliance on informal and individualised approaches to knowledge selection, adaptation, and sharing. These insights underscore a broader systemic issue: the lack of structured support for knowledge management and the inconsistent use of academic research in decision-making. Teachers frequently adapt materials based on personal judgement rather than systematic evidence, pointing to the need for more robust frameworks that integrate evidence-based practices with innovative teaching methods. The paper then turns to some of the issues surrounding use of AI, proposing a framework for system-level learning that includes data analysis, evidence synthesis, and the deliberate orchestration of knowledge sharing. It emphasises the necessity of creating environments that validate and disseminate educational knowledge effectively. Without a clear responsibility for system-level learning, education systems risk missing out on the benefits of collective intelligence, leaving valuable innovations and insights underutilised.