Discovering the curriculum with AI: A proof-of-concept demonstration with an intelligent tutoring system for teaching project selection
This work addresses the challenge of teaching effective decision-making to executives in organizations, representing an incremental extension from simplified tasks to a real-world application.
The authors tackled the problem of suboptimal decision-making in real-world project selection by developing an AI method (MGPS) that discovers and teaches optimized strategies, resulting in MGPS outperforming a state-of-the-art method and significantly improving human learning in a training experiment.
The decisions of individuals and organizations are often suboptimal because fully rational decision-making is too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach clever heuristics. So far, this line of research has been limited to simplified, artificial decision-making tasks. This article is the first to extend this approach to a real-world decision problem, namely, executives deciding which project their organization should launch next. We develop a computational method (MGPS) that automatically discovers project selection strategies that are optimized for real people, and we develop an intelligent tutor that teaches the discovered project selection procedures. We evaluated MGPS on a computational benchmark and tested the intelligent tutor in a training experiment with two control conditions. MGPS outperformed a state-of-the-art method and was more computationally efficient. Moreover, people who practiced with our intelligent tutor learned significantly better project selection strategies than the control groups. These findings suggest that AI could be used to automate the process of discovering and formalizing the cognitive strategies taught by intelligent tutoring systems.