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TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring — arXiv:2608.03952

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03952
License (as archived): CC0-1.0
Archived: 2026-10-31T12:17:15.108Z
SHA-256 of the archived text: 75a3da4f052025d024b4fa2965da4c17666f4322e2c3b5b024bbf200332bc0b3
Stable URL of this page: https://metraipolis.com/documento/dc_dfd246d1297b

How to cite

TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring — arXiv:2608.03952 — arXiv (en), https://arxiv.org/abs/2608.03952 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dfd246d1297b (Metraipolis archive dc_dfd246d1297b, sha256 75a3da4f052025d024b4fa2965da4c17666f4322e2c3b5b024bbf200332bc0b3)

BibTeX

@misc{dcdfd246d1297b,
  title = {TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring — arXiv:2608.03952},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03952},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dfd246d1297b; sha256 75a3da4f052025d024b4fa2965da4c17666f4322e2c3b5b024bbf200332bc0b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03952. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.03952 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:05:52.867Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).