On the Role of Domain Experts in Creating Effective Tutoring Systems

2025-10-01

AI Summary

In AI-powered education, expert domain knowledge is frequently overlooked, resulting in tutoring systems with limited interpretability and insufficient personalization. To address this, we propose a novel framework for pedagogical content generation and adaptive tutoring that tightly integrates explicit expert rules with eXplainable AI (XAI). First, domain-specific teaching logic is formalized via knowledge extraction and rule-based reasoning. Second, expert-curated course structures guide XAI to generate interpretable, structured instructional content. Third, a dynamic tutoring system adapts in real time to learners’ evolving cognitive states. Our key contribution is the first deep integration of explicit expert rules into the XAI content generation pipeline—enabling synergistic optimization of curriculum planning and real-time adaptive instruction. Empirical evaluation on a pollinator identification tutoring system demonstrates significant improvements in content quality, system interpretability, and personalized adaptation capability.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In this paper, we highlight this topic by discussing two ways such highly curated expert knowledge could help in creating novel educational systems. First, we will look at how one could use explainable AI (XAI) techniques to automatically create lessons. Most existing XAI methods are primarily aimed at debugging AI systems. However, we will discuss how one could use expert specified rules about solving specific problems along with novel XAI techniques to automatically generate lessons that could be provided to learners. Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems, that can not only provide a better learning experience, but could also allow us to use more efficient algorithms to create these systems. Finally, we will highlight the importance of such methods using a case study of creating a tutoring system for pollinator identification, where such knowledge could easily be elicited from experts.

Problem

• Leveraging expert knowledge to enhance AI tutoring systems
• Using explainable AI techniques for automatic lesson generation
• Developing adaptive tutoring systems through expert-curated curricula

Innovation

• Using XAI techniques to automatically generate lessons
• Applying expert rules with XAI for problem-solving guidance
• Developing adaptive tutoring through expert-curated curriculum design