Machine learning algorithms for climate change integration in evaluation: A conceptual model and simulated case application in Africa

Background: Climate change increasingly shapes development outcomes, necessitating its integration into evaluation processes. In Africa, recurrent droughts, floods and temperature extremes disrupt progress across sectors. Conventional evaluation approaches rely on static indicators and linear analyses, constraining their capacity to capture climate variability or explain how environmental shocks affect programme performance. Objectives: This study seeks to develop and assess an artificial intelligence (AI)-driven evaluation framework that integrates climate indicators into development evaluation systems to enhance analytical precision, relevance and timeliness. Method: A machine learning (ML) algorithm is proposed to jointly process climate and development data, enabling pattern recognition, correlation analysis and predictive modelling. Publicly available climate and education datasets from East and South Africa are used to demonstrate the framework’s application. Results: Findings indicate that climate anomalies are associated with observable changes in school attendance and learning outcomes. The model demonstrates the utility of AI in identifying climate–development linkages and generating more timely insights than traditional methods. Conclusion: The integration of AI into evaluation systems improves the capacity to analyse climate-related impacts on development outcomes, supporting more informed and adaptive planning. Contribution: The study provides a scalable technical model for climate-responsive evaluation, demonstrating how ML can operationalise climate–development relationships to strengthen evidence-based decision-making before or after programme performance in affected contexts.