Applications of AI in Support of Theory-Driven Evaluation

As AI tools become more accessible and capable, evaluators are exploring their transformative potential across evaluation practice. This session brings together four perspectives on how AI can support and enhance evaluation. Presenters will demonstrate how AI has clarified and evolved program theories by integrating stakeholder insights with external theory, tested logic models through heat mapping, and simulated debates among AI agents to assess competing viewpoints. Applications in evaluation education will be shared, including how AI promotes engagement, supports asynchronous learning, and aids curriculum development. Another explores AI-enhanced data analysis using Google Sheets, highlighting adaptive strategies based on AI-generated insights, voice translations, and podcast summaries. The session will also spotlight how human–AI collaboration can elevate theory-driven evaluations by refining logic models, clarifying causal pathways, and accelerating reporting. These presentations offer practical tips, ethical reflections, and strategies for leveraging AI in service of more insightful and inclusive evaluation.
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