@inproceedings{li-etal-2026-longtutor,
    title = "{L}ong{T}utor: Benchmarking Large Language Models for Long-term Personalized Tutoring",
    author = "Li, Ning  and
      Zhang, Zheng  and
      Huang, Zhenya  and
      Li, Rui  and
      Zhan, Yi  and
      Luo, Yinbo  and
      Liu, Qi  and
      Chen, Enhong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1371/",
    pages = "29712--29737",
    ISBN = "979-8-89176-390-6",
    abstract = "The rapid advancement of large language models (LLMs) has driven the deployment of LLM-based AI tutors on online learning platforms. This widespread adoption highlights an urgent need for systematic benchmarks to evaluate their tutoring capabilities. However, existing evaluations predominantly focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning. To bridge this gap, we introduce LongTutor, a benchmark for long-term personalized tutoring grounded in formative assessment theory. Built from expert-annotated real-world learning logs, LongTutor evaluates LLMs across three progressive tasks: historical evidence acquisition, knowledge state diagnosis, and adaptive teaching action. Our experiments reveal a critical capability mismatch: while LLMs excel at evidence acquisition, they struggle to effectively leverage long-term history for accurate diagnosis and adaptive teaching. To enable scalable benchmark expansion, we further propose an automated generator{--}verifier pipeline, paving the way toward truly long-term AI tutoring systems."
}