Measuring Optimal Challenge: Trajectory-Based Difficulty Alignment in Open-Ended Language Tutoring
Year: 2026
Type: conference-paper
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 κ = 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 capabilityshift 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.
Authors: Ziqi Shu, Shuman Wang, Michael Hardy
Institution: Stanford University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166870552