HTN-Based Tutors: A New Intelligent Tutoring Framework Based on Hierarchical Task Networks
This work addresses the problem of personalized learning in education by offering an incremental improvement in tutoring frameworks.
The authors tackled the challenge of granularity in intelligent tutoring systems by proposing a new framework based on Hierarchical Task Networks (HTNs), which enables adaptive scaffolding and aligns with skill compositionality.
Intelligent tutors have shown success in delivering a personalized and adaptive learning experience. However, there exist challenges regarding the granularity of knowledge in existing frameworks and the resulting instructions they can provide. To address these issues, we propose HTN-based tutors, a new intelligent tutoring framework that represents expert models using Hierarchical Task Networks (HTNs). Like other tutoring frameworks, it allows flexible encoding of different problem-solving strategies while providing the additional benefit of a hierarchical knowledge organization. We leverage the latter to create tutors that can adapt the granularity of their scaffolding. This organization also aligns well with the compositional nature of skills.