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

TL;DR

This paper introduces HTN-based tutors, a novel intelligent tutoring framework that uses Hierarchical Task Networks to enhance knowledge representation, adapt scaffolding granularity, and better align with skill composition for personalized learning.

Contribution

It presents a new framework utilizing HTNs for flexible, hierarchical knowledge modeling in intelligent tutors, improving adaptability and skill organization.

Findings

Hierarchical organization enables adaptive scaffolding.

Supports flexible encoding of problem-solving strategies.

Aligns well with skill composition principles.

Abstract

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.