@inproceedings{shu-etal-2026-measuring,
    title = "Measuring Optimal Challenge: Trajectory-Based Difficulty Alignment in Open-Ended Language Tutoring",
    author = "Shu, Ziqi  and
      Wang, Shuman  and
      Hardy, Michael",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.46/",
    pages = "651--667",
    ISBN = "979-8-89176-409-5",
    abstract = "Conversational English as a Foreign Language (EFL) tutoring relies on dynamically generated exercises rather than fixed item banks, so traditional difficulty estimation cannot verify whether a task is appropriately calibrated to a learner. We propose a framework that measures difficulty alignment directly from observable interactional behavior, classifying each exercise into one of three states (Under-Challenged, Optimally Challenged, or Over-Challenged) based on turn-level sequences of student attempts, errors, confusion, and tutor scaffolding. Using 1,566 exercises from the Teacher-Student Chatroom Corpus, we validate the classification against human annotation (Cohen{'}s kappa = 0.79 at the state level) and show that a learner{'}s cumulative trajectory of these states predicts success on subsequent exercises. Aggregating these predictions into a within-session capability-shift proxy, we find that sessions with higher proportions of over-challenging exercises systematically yield lower estimated shifts, while optimally challenging interactions are significantly associated with greater improvement than under-challenging ones {---} patterns consistent with Krashen{'}s Input Hypothesis."
}