This study evaluates machine-learning classifiers for detecting lower justification depth in student responses from AI-supported instructional design tasks. Using 600 human-coded responses and 20 group-aware repeated train-development-test partitions, RoBERTa outperformed TF-IDF logistic regression and Complement Naive Bayes under recall-prioritized thresholding for formative assessment support.