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cs.CL2026

Domain-Adapted Retrieval for In-Context Annotation of Pedagogical Dialogue Acts

Jinsook Lee, Kirk Vanacore, Zhuqian Zhou +2

Automated annotation of pedagogical dialogue is a high-stakes task where LLMs often fail without sufficient domain grounding. We present a domain-adapted RAG pipeline for tutoring…

cs.CL2026

Tutor Move Taxonomy: A Theory-Aligned Framework for Analyzing Instructional Moves in Tutoring

Zhuqian Zhou, Kirk Vanacore, Tamisha Thompson +2

Understanding what makes tutoring effective requires methods for systematically analyzing tutors' instructional actions during learning interactions. This paper presents a tutor mo…

cs.CL2026

LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse

Bakhtawar Ahtisham, Kirk Vanacore, Zhuqian Zhou +2

Large Language Models (LLMs) are increasingly deployed to automatically label and analyze educational dialogue at scale, yet current pipelines lack reliable ways to detect when mod…

cs.CL2026

Codebook-Injected Dialogue Segmentation for Multi-Utterance Constructs Annotation: LLM-Assisted and Gold-Label-Free Evaluation

Jinsook Lee, Kirk Vanacore, Zhuqian Zhou +3

Dialogue Act (DA) annotation typically treats communicative or pedagogical intent as localized to individual utterances or turns. This leads annotators to agree on the underlying a…

cs.CL2025

How well do Large Language Models Recognize Instructional Moves? Establishing Baselines for Foundation Models in Educational Discourse

Kirk Vanacore, Rene F. Kizilcec

Large language models (LLMs) are increasingly adopted in educational technologies for a variety of tasks, from generating instructional materials and assisting with assessment desi…