PedagoSense: A Pedology Grounded LLM System for Pedagogical Strategy Detection and Contextual Response Generation in Learning Dialogues
Shahem Sultan, Shahem Fadi, Yousef Melhim, Ibrahim Alsarraj, Besher Hassan

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…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Innovative Teaching and Learning Methods · Topic Modeling
