EduBehaviors: Assertion-based Schemas for Auditable Coding of Educational Dialogues

arXiv:2609.27043 · cs.CL, cs.CY, cs.LG · Submitted 2026-09-22 · Read on arXiv

cs.CL, cs.CY, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/scale-nssa/edubehaviors-studio

Terminology

• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse
• Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale
• Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
• Automated Annotation with Generative AI Requires Validation
• Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark
• Efficient Few-Shot Learning Without Prompts
• How well do Large Language Models Recognize Instructional Moves? Establishing Baselines for Foundation Models in Educational Discourse

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