Toward Scenario Adaptation for Learning
James Niehaus, Mark Riedl · Frontiers in artificial intelligence and applications · 2009
This paper presents a methodology for automatically customizing a scenario to suit a learner's abilities, needs, or goals. Training scenarios are often utilized to give learners hands-on experience with real-life problem solving tasks. The customization of scenarios has the potential to improve learning gains within these domains. We present initial steps toward an intelligent technology called a Scenario Adaptor that employs a partial order planning formalism to reason about learning objectives and causality, and we discuss how the Scenario Adaptor may add or delete learning objectives from a scenario.