Expanding on previous research highlighting the learning benefits of errors, this study explores the enduring effects of error-induced learning. Using an adaptive fact-learning system, 23 participants engaged in recognition, recall, and error tasks, with repeated testing for memory assessment. Initial findings echoed previous results: items learned through errors initially took longer to retrieve. However, a significant shift occurred over time; error items demonstrated faster retrieval speeds compared to study items, and, most notably, they exhibited greater resilience against forgetting. This study reaffirms the positive role of errors in learning and uncovers their contribution to enhanced long-term memory retention. These insights challenge traditional learning paradigms, advocating for an educational approach that recognizes and leverages the value of errors in learning processes.

Recent studies suggest that errors facilitate learning in certain conditions. Despite this, reinforcement paradigms dominate learning methods, subscribing to the narrative that errorless learning is the foundation of an ideal learning environment. If we continue to view learning from this restrictive perspective, we may fail to capture and apply the benefits of errors. In this paper, we investigate two potential mechanisms of post-error learning. Participants (N = 61) learned word pairs in either a study or error trial before taking a final test. Supporting past error learning literature, errors before a study opportunity led to better performance on a final test. Differences in reaction times between conditions support the theory that errors increase learning by acting as a mediator, or secondary cue, to the correct answer on subsequent tests.

Computational models of memory used in adaptive learning settings trace a learner's memory capacities. However, less work has been done on the implementation of these models in the clinical realm. Current assessment tools lack the reliable, convenient, and repeatable qualities needed to capture the individualized and evolving nature of memory decline. The goal of this project was to predict and track memory decline in subjectively- or mildly cognitively impaired (MCI) individuals by using a model-based, adaptive fact-learning system. Here, we present data demonstrating that these tools can diagnose mild memory impairment with over 80% accuracy after a single 8-minute learning session. These findings provide new insights into the nature and progression of memory decline and may have implications for the early detection and management of Alzheimer's disease and other forms of dementia.