PedagoSense: A Pedology Grounded LLM System for Pedagogical Strategy Detection and Contextual Response Generation in Learning Dialogues

TL;DR

PedagoSense is a system that detects pedagogical strategies in learning dialogues and generates contextually appropriate responses using large language models, aiming to enhance interaction quality in educational conversations.

Contribution

It introduces a novel two-stage classification and response generation system grounded in pedology, integrating pedagogical theory with LLMs for adaptive educational dialogue.

Findings

High accuracy in pedagogical strategy detection

Data augmentation improves detection performance

Challenges remain in classifying fine-grained strategies

Abstract

This paper addresses the challenge of improving interaction quality in dialogue based learning by detecting and recommending effective pedagogical strategies in tutor student conversations. We introduce PedagoSense, a pedology grounded system that combines a two stage strategy classifier with large language model generation. The system first detects whether a pedagogical strategy is present using a binary classifier, then performs fine grained classification to identify the specific strategy. In parallel, it recommends an appropriate strategy from the dialogue context and uses an LLM to generate a response aligned with that strategy. We evaluate on human annotated tutor student dialogues, augmented with additional non pedagogical conversations for the binary task. Results show high performance for pedagogical strategy detection and consistent gains when using data augmentation, while analysis highlights where fine grained classes remain challenging. Overall, PedagoSense bridges pedagogical theory and practical LLM based response generation for more adaptive educational technologies.