# HTN-Based Tutors: A New Intelligent Tutoring Framework Based on Hierarchical Task Networks

arXiv: https://arxiv.org/abs/2405.14716 · HTML: https://mlsanity.com/paper/2405.14716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 26 references that are in the map.
- First posted on arXiv 23 May 2024.

## Built on by (most cited first, 3 of 3)

- [TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students](https://mlsanity.com/paper/2505.01563/) (2025)
- [AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent](https://mlsanity.com/paper/2411.17924/) (2024)
- [A4L: An Architecture for AI-Augmented Learning](https://mlsanity.com/paper/2505.06314/) (2025)

## Abstract (arXiv)

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.