Improving Procedural Skill Explanations via Constrained Generation: A Symbolic-LLM Hybrid Architecture

• URL: http://arxiv.org/abs/2511.20942v1
• Date: Wed, 26 Nov 2025 00:29:53 GMT
• Title: Improving Procedural Skill Explanations via Constrained Generation: A Symbolic-LLM Hybrid Architecture
• Authors: Rahul Dass, Thomas Bowlin, Zebing Li, Xiao Jin, Ashok Goel,
• Abstract summary: In procedural skill learning, instructional explanations must convey not just steps, but the causal, goal-directed, and compositional logic behind them.<n>We present Ivy, an AI coaching system that delivers structured, multi-step explanations by combining symbolic Task-Method-Knowledge (TMK) models with a generative interpretation layer-an generative interpretation layer-that constructs explanations while being constrained by TMK structure.
• Score: 2.6141337419964725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In procedural skill learning, instructional explanations must convey not just steps, but the causal, goal-directed, and compositional logic behind them. Large language models (LLMs) often produce fluent yet shallow responses that miss this structure. We present Ivy, an AI coaching system that delivers structured, multi-step explanations by combining symbolic Task-Method-Knowledge (TMK) models with a generative interpretation layer-an LLM that constructs explanations while being constrained by TMK structure. TMK encodes causal transitions, goal hierarchies, and problem decompositions, and guides the LLM within explicit structural bounds. We evaluate Ivy against responses against GPT and retrieval-augmented GPT baselines using expert and independent annotations across three inferential dimensions. Results show that symbolic constraints consistently improve the structural quality of explanations for "how" and "why" questions. This study demonstrates a scalable AI for education approach that strengthens the pedagogical value of AI-generated explanations in intelligent coaching systems.

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