Discovering the curriculum with AI: A proof-of-concept demonstration with an intelligent tutoring system for teaching project selection
Lovis Heindrich, Falk Lieder

TL;DR
This paper demonstrates how AI can discover and teach effective project selection heuristics in real-world decision-making, improving learning outcomes through an intelligent tutoring system.
Contribution
It introduces MGPS, a novel computational method for discovering project selection strategies optimized for humans, and develops an intelligent tutor to teach these strategies.
Findings
MGPS outperforms existing methods in efficiency and effectiveness.
Participants trained with the intelligent tutor learned better strategies.
The approach extends AI-assisted decision strategy discovery to real-world problems.
Abstract
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…
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Taxonomy
TopicsBig Data and Business Intelligence · Software Engineering Techniques and Practices
