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 ≈ 1,000 synthetic administrations, the AI composite improved impairment detection over age-adjusted traditional scoring: ROC AUC 0.859–0.860 vs 0.841 (ΔAUC ≈ 0.018–0.019) and average precision 0.872–0.873 vs 0.813 (ΔAP ≈ 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.