Standard list-learning tasks such as the Rey Auditory Verbal Learning Test (RAVLT) and the California Verbal Learning Test (CVLT) have underpinned memory assessment for decades, yet their scoring remains narrow--reducing rich recall behavior to a single number. We present an artificial intelligence (AI) system that transforms a traditional word list recall into a multi-dimensional cognitive profile by automatically detecting memory strategies, analyzing temporal dynamics, quantifying organizational quality, and measuring speech-derived confidence. In a simulation study spanning 15-17 realistic recall scenarios and N approximately 1,000 synthetic administrations, the AI composite improved impairment detection over age-adjusted traditional scoring: ROC AUC 0.859-0.860 vs 0.841 (delta-AUC approximately 0.018-0.019) and average precision 0.872-0.873 vs 0.813 (delta-AP approximately 0.060). Gains were largest in borderline cases (e.g., compensated impairment and efficient low-recall profiles). While clinical validation is pending, these results demonstrate algorithmic feasibility for mobile, on-device screening and motivate prospective trials in Mild Cognitive Impairment (MCI)-focused cohorts.
Kevin Mekulu (Sun,) studied this question.
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