Generative Artificial Intelligence, Health Risk Perception Bias, and Sports Participation Among Older Adults —An Empirical Investigation Grounded in the Search-as-Learning Paradigm

Abstract Against the backdrop of the deep integration of population aging and digital intelligent transformation, leveraging artificial intelligence technologies to promote sports participation among older adults constitutes a critical step toward the digital transformation of healthy aging and the high-quality development of sports for older adults. Using two waves of panel data from the China Longitudinal Aging Social Survey spanning 2020–2023, and drawing on search-as-learning theory, this study empirically examines the effect of generative artificial intelligence on older adults’ sports participation and the underlying mechanisms. The findings indicate that generative AI significantly promotes sports participation among older adults, a result that holds after a battery of robustness checks. The mechanism analysis reveals that generative AI fosters older adults’ sports participation by reducing health risk perception bias. In particular, compared to health risk perception bias based on peer comparison, generative AI exerts a stronger corrective effect on health risk perception bias based on self-comparison. Extended analyses show that the impact of generative AI on older adults’ sports participation exhibits significant heterogeneity based on prior knowledge level, task type, and exercise motivation. This study aims to provide policy recommendations for government agencies on leveraging digital and intelligent technologies to enhance older adults’ awareness of proactive health management and to encourage their sustained participation in physical exercise.