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.