This study evaluates an AI tutoring tool in a pre-licensure exam preparation product. Using residual gain modeling, elastic-net feature selection, and Gaussian Mixture clustering on LLM-derived cognitive fingerprints, the study identifies meaningful learner profiles and shows that the cognitive depth of student–AI interaction, not volume, drives measurable learning gain.