Computational memory models have proven effective in adaptive learning environments for assessing learners' memory capabilities. However, these models have not been widely applied in clinical settings. Evaluation of memory loss still heavily relies on extensive neuropsychological testing performed by neurologists or psychiatrists, especially in the context of progressive neurodegenerative disorders. Current evaluation tools lack the necessary reliability, convenience, and repeatability to effectively capture key dynamics of memory decline, including the unique and changing nature of memory over time. The goal of this study was to predict and monitor memory decline in individuals diagnosed with Mild Cognitive Impairment (MCI) using a model-based adaptive fact learning system. Participants, aged 55 to 85 years, were divided into two groups based on their cognitive classification and completed weekly online learning assessments at home, tracking their individual Speed of Forgetting (SoF) across various study materials. The results offer compelling evidence that these tools can diagnose mild memory impairment with an impressive accuracy rate of over 85% in just an 8-minute learning session. These findings not only enhance our understanding of the nature and progression of memory decline but also bear significant implications for the early detection and management of conditions such as Alzheimer's disease and other forms of dementia.