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%0 Conference Proceedings
%T Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors
%A Wang, Jian
%A Dai, Yinpei
%A Zhang, Yichi
%A Ma, Ziqiao
%A Li, Wenjie
%A Chai, Joyce
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-training
%X Intelligent tutoring agents powered by large language models (LLMs) have been increasingly explored to deliver personalized knowledge in areas such as language learning and science education. However, their capabilities in guiding users to solve complex real-world tasks remain underexplored. To address this limitation, in this work, we focus on coding tutoring, a challenging problem that requires tutors to proactively guide students towards completing predefined coding tasks. We propose a novel agent workflow, Trace-and-Verify (TRAVER), which combines knowledge tracing to estimate a student’s knowledge state and turn-by-turn verification to ensure effective guidance toward task completion. We introduce DICT, an automatic evaluation protocol that assesses tutor agents using controlled student simulation and code generation tests. Extensive experiments reveal the challenges of coding tutoring and demonstrate that TRAVER achieves a significantly higher success rate. Although we use code tutoring as an example in this paper, our approach can be extended beyond coding, providing valuable insights into advancing tutoring agents for human task learning.
%R 10.18653/v1/2025.findings-acl.642
%U https://aclanthology.org/2025.findings-acl.642/
%U https://doi.org/10.18653/v1/2025.findings-acl.642
%P 12416-12436

Markdown (Informal)

[Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors](https://aclanthology.org/2025.findings-acl.642/) (Wang et al., Findings 2025)

• Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors (Wang et al., Findings 2025)

ACL

• Jian Wang, Yinpei Dai, Yichi Zhang, Ziqiao Ma, Wenjie Li, and Joyce Chai. 2025. Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12416–12436, Vienna, Austria. Association for Computational Linguistics.