Authors: Zilong Wang, Nan Chen, Luna K. Qiu, Ling Yue, Geli Guo, Yang Ou, Shiqi Jiang, Yuqing Yang, Lili Qiu
Abstract: In recent years, the rapid aging of the global population has led to an
increase in cognitive disorders, such as Alzheimer’s disease, presenting
significant public health challenges. Although no effective treatments
currently exist to reverse Alzheimer’s, prevention and early intervention,
including cognitive training, are critical. This report explores the potential
of AI chatbots in enhancing personalized cognitive training. We introduce ReMe,
a web-based framework designed to create AI chatbots that facilitate cognitive
training research, specifically targeting episodic memory tasks derived from
personal life logs. By leveraging large language models, ReMe provides enhanced
user-friendly, interactive, and personalized training experiences. Case studies
demonstrate ReMe’s effectiveness in engaging users through life recall and
open-ended language puzzles, highlighting its potential to improve cognitive
training design. Despite promising results, further research is needed to
validate training effectiveness through large-scale studies that include
cognitive ability evaluations. Overall, ReMe offers a promising approach to
personalized cognitive training, utilizing AI capabilities to meet the growing
demand for non-pharmacological interventions in cognitive health, with future
research aiming to expand its applications and efficacy.
Source: http://arxiv.org/abs/2410.19733v1
