Ontology-driven Reinforcement Learning for Personalized Student Support
This addresses the challenge for educators who lack time and resources to provide individualized support in classrooms, though it appears incremental as it builds on existing AI methods.
The paper tackles the problem of personalizing student support in virtual educational systems by introducing a framework that combines ontologies with multi-agent reinforcement learning, resulting in a modular system adaptable to any educational software.
In the search for more effective education, there is a widespread effort to develop better approaches to personalize student education. Unassisted, educators often do not have time or resources to personally support every student in a given classroom. Motivated by this issue, and by recent advancements in artificial intelligence, this paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system such as a serious game or an intelligent tutoring system. To fit any educational situation, we apply ontologies for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students.