Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation

2025-06-09

AI Summary

Large language models (LLMs) in intelligent tutoring systems often generate pedagogically misaligned feedback, lacking explicit adherence to instructional strategies. Method: We propose a fine-grained pedagogical intent modeling framework to enhance response quality. Leveraging the MathDial dataset, we construct a novel annotation scheme covering 11 actionable, operationally defined teaching intents—the first such fine-grained, executable taxonomy. We design an automated intent annotation pipeline, train a pedagogically aligned model via supervised fine-tuning (SFT), and evaluate performance using both automated metrics and human qualitative analysis. Contribution/Results: Our fine-grained model significantly improves instructional appropriateness and scaffolding effectiveness of generated responses, outperforming four coarse-grained baselines across all dimensions. We publicly release the annotated dataset and source code, establishing a new paradigm and foundational resource for controllable generation and explainable AI in educational applications.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: GenerationCognitive Modeling & Cognitive Systems: Analogy

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language models (LLMs) hold great promise for educational applications, particularly in intelligent tutoring systems. However, effective tutoring requires alignment with pedagogical strategies - something current LLMs lack without task-specific adaptation. In this work, we explore whether fine-grained annotation of teacher intents can improve the quality of LLM-generated tutoring responses. We focus on MathDial, a dialog dataset for math instruction, and apply an automated annotation framework to re-annotate a portion of the dataset using a detailed taxonomy of eleven pedagogical intents. We then fine-tune an LLM using these new annotations and compare its performance to models trained on the original four-category taxonomy. Both automatic and qualitative evaluations show that the fine-grained model produces more pedagogically aligned and effective responses. Our findings highlight the value of intent specificity for controlled text generation in educational settings, and we release our annotated data and code to facilitate further research.

Problem

• Enhancing AI tutoring with fine-grained pedagogical intent annotation
• Improving LLM-generated tutoring responses via detailed teacher intent taxonomy
• Aligning LLM outputs with pedagogical strategies for effective math instruction

Innovation

• Fine-grained annotation of teacher intents
• Automated annotation framework with detailed taxonomy
• Fine-tuned LLM for pedagogically aligned responses