With the increasing diversification and personalization of elderly care service needs among retired university employees, existing service models often rely on manual management, resulting in limited service content, delayed responses, and a lack of personalized support. Therefore, this study introduces generative artificial intelligence (AI) to empower elderly care service management. Combining intelligent recommendations with interactive support mechanisms, this study explores detailed improvements in learning engagement, health management, and daily life services to enhance the precision and acceptability of services. A randomized controlled trial design is used, randomly dividing 160 retired university employees into an intervention group and a control group (80 participants in each group). The intervention group receives conventional elderly care services supplemented with generative AI functionality. The intervention group shows significant improvements in learning engagement, self-assessment of health, and platform satisfaction. Learning engagement increases from 4.2 to 4.7; self-assessment of health increases from 3.4 to 3.9; platform satisfaction increases from 4.0 to 4.5. The control group shows significantly smaller increases. Regarding physical health, the intervention group experiences an average decrease in systolic blood pressure of 4 mmHg, and sleep duration increases from 6.8 to 7.2 hours. Qualitative analysis shows that users generally appreciate the platform's trustworthiness and ease of use, but at the same time, they express some concerns about data privacy and reliance on AI for decision-making.

Chuanwu Ji. Exploration of the Path for Empowering the Elderly Care Service Management of Retired College Staff with Generative Artificial Intelligence. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 165-173. https://doi.doi.org/10.3807/IJSSEM.2026.070117.

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