Franzen, SamuelQuang, CuongSchweizer, LukasBudzier, AlexanderHrstich, PeterReissfelder, StéphaneGold, JennyVellez, MercedesRamirez, SantiagoRaimondo, Estelle2022-03-092022-03-092022https://hdl.handle.net/10986/37117This pilot study tests the applicability, usefulness, and added value of using AI for advanced theory-based content analysis within the framework of IEG’s thematic evaluations. Using a set of interventions associated with the World Bank’s chronic malnutrition and stunted growth portfolio, the paper assesses the efficiency gains generated by AI-assisted content analysis in labeling and classifying text according to an outcome-based conceptual framework.CC BY 3.0 IGOCOMPLEX PROGRAM EVALUATIONDEVELOPMENT EVALUATION CHALLENGESTHEORY-DRIVEN EVALUATIONAUTOMATE CONTENT ANALYSISAUTOMATE QUALITATIVE SYNTHESISMACHINE LEARNINGARTIFICIAL INTENNIGENCE (AI)Working PaperWorld Bank10.1596/IEG167127