Work

Ontology-Driven Reinforcement Learning for Personalized Student Support

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

DOI: 10.1109/smc54092.2024.10832036

Authors: Ryan Hare, Ying Tang

Institution: Rowan University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funder: National Science Foundation