Current Status and Associated Factors of Artificial Intelligence-Based Personalized Learning for Health Management in Older Adults

Artificial intelligence (AI)-based approaches have been increasingly integrated into the field of health management for older adults, expanding opportunities for personalized interventions.This review aimed to examine the current status of AI-based personalized learning in older adults' health management and identify the factors influencing participation and learning effectiveness in order to derive implications for tailored learning design.A descriptive review of previous studies on AI-based health management, care, digital health, education, and learning interventions targeting older adults was conducted.AI-based interventions were applied across diverse domains, including chronic disease self-management, maintenance of cognitive function, support for activities of daily living, health information education, emotional support, and reduction of social isolation.Most interventions incorporated core elements of personalized learning including individual characteristic-based tailoring, repetitive interactions, and data-driven feedback.Participation and effectiveness were shaped by the interaction of individual factors (e.g., age, cognitive and health status, digital literacy, technological self-efficacy), social and environmental factors (e.g., social connectedness and accessibility of learning environments), and technological factors (e.g., interface design, degree of personalization, and ethical trustworthiness).These findings emphasize the need to reconceptualize AI-based interventions in older adults' health management as learning-oriented approaches and to develop designs that reflect cognitive and emotional characteristics, integrate digital literacy, and ensure equitable and ethical technology implementation.