这篇论文把教学策略拆成13种，用260段真实对话和3.2万条标注训练出TACTutor，在78个测试场景比基座涨了20%，盲测还赢过商业模型。
论文提出TACT框架，用于训练和评估教学自适应的英语辅导模型。该框架包含13种辅导策略的Tutor-Strategy分类法和描述学生行为的Student-Move分类法。基于两套分类法构建的TACTCorpus覆盖260段师生对话，含32,379条标注。后训练的TACTutor在TACTBench（78个场景）上比基座模型Qwen3.5-4B提升20.30%，并在50名学习者的盲测中获得最高评分。
TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring
Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners. Effective ESL tutoring, however, requires more than fluent response generation: a tutor must select an appropriate pedagogical action based on learner behavior and dialogue context. Human-tutoring research offers principles for adaptive support, but they are often task-specific and remain insufficiently integrated into LLM-based ESL tutor training and evaluation. We present TACT (Taxonomy-Aligned Conversational Tutor), a human-grounded framework for post-training and evaluating pedagogically adaptive ESL tutors. Drawing on established literature, we develop two complementary taxonomies: the Tutor-Strategy Taxonomy with 13 tutor response strategies and the Student-Move Taxonomy characterizing learner behavior by move type and status. Using these taxonomies, we construct TACTCorpus, which enriches 260 authentic teacher-student conversations with 32,379 annotations and quality-controlled augmented training data. We then post-train Qwen3.5-4B through supervised fine-tuning followed by taxonomy-aligned Group Relative Policy Optimization, producing TACTutor and optimizing it for scaffolding quality rather than reference imitation alone. On TACTBench, a strategy-balanced diagnostic benchmark comprising 78 authentic tutoring contexts, TACTutor improves over its backbone by 20.30% and outperforms all evaluated proprietary baselines under the same protocol, while maintaining backbone performance on established external educational benchmarks; in a blinded study with 50 learners, it also receives the highest overall mean rating among the evaluated tutors. We release the data, benchmark, and model weights, providing an open foundation for developing pedagogically adaptive ESL tutors.