AI-Enhanced Memory List Assessment System: Multi-Dimensional Analysis and Automated Strategy Detection for Next-Generation Cognitive Screening

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 multidimensional 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 {approx} 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{approx} 0.018-0.019) and average precision 0.872-0.873 vs 0.813 ({Delta}AP{approx} 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.