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.

翻译：智能导师系统在提供个性化与适应性学习体验方面已展现出显著成效。然而，现有框架在知识表示粒度及其所能提供的教学指导方面仍面临挑战。为应对这些问题，我们提出基于分层任务网络（HTN）的智能导师框架，该框架利用分层任务网络表示专家模型。与其他教学框架类似，本框架支持对多种问题解决策略进行灵活编码，同时具备分层知识组织带来的附加优势。我们利用这一分层特性构建能够自适应调整教学支架粒度的导师系统。该知识组织结构亦与技能的组合性质高度契合。