A Framework for Proficiency-Aligned Grammar Practice in LLM-Based Dialogue Systems

Communicative practice is critical for second language development, yet learners often lack targeted, engaging opportunities to use new grammar structures.While large language models (LLMs) can offer coherent interactions, they are not inherently aligned with pedagogical goals or proficiency levels.In this paper, we explore how LLMs can be integrated into a structured framework for enabling goaloriented, grammar-focused interaction, building on an existing dialogue system.Through controlled simulations, we evaluate five LLMs across 75 A2-level tasks under two conditions: (i) grammar-targeted, task-anchored prompting and (ii) the addition of a lightweight postgeneration validation pipeline using a grammar annotator.Our findings show that templatebased prompting alone substantially increases target-form coverage up to 91.4% for LLaMA 3.1-70B-Instruct, while reducing overly advanced grammar usage.The validation pipeline provides an additional boost in form-focused tasks, raising coverage to 96.3% without significantly degrading appropriateness.