Digital Intelligence Empowers Rural Aging Human Resource Development: DST Model Construction and Path Design

Xinru Han, Ge Wen (), Junhong Xu, Youyu Zhang and Baisong Chen
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Xinru Han: Chengdu University of Information Technology, School of Management
Ge Wen: Chengdu University of Information Technology, School of Management
Junhong Xu: Chengdu University of Information Technology, School of Management
Youyu Zhang: Chengdu University of Information Technology, School of Management
Baisong Chen: Chengdu University of Information Technology, School of Management

A chapter in Proceedings of the 2026 4th International Conference on Digital Economy and Management Science (CDEMS 2026), 2026, pp 145-152 from Springer

Abstract: Abstract Activating rural aging human resources is crucial for addressing labor shortages in rural areas and supporting rural revitalization. Under the digital-intelligent transformation context, developing this demographic faces structural challenges such as organizational fragmentation and supply-demand mismatches. Based on digital empowerment and diffusion theory, this study examines the practical needs and obstacles in digital-intelligent development for rural elderly workers using Sichuan Province as a case study. It proposes a three-dimensional DST development model integrating Digital Platform (D), Targeted Training (S), and Skill Transfer (T). The model resolves organizational challenges through cross-sector collaborative platforms, implements differentiated training via demographic profiling, facilitates localized skill dissemination through rural social networks, and achieves dynamic optimization via feedback loops. The DST model provides a systematic theoretical framework for enhancing rural human capital quality in the digital economy era, while offering actionable pathways for activating aging workforce potential and empowering rural revitalization in central and western China.

Keywords: Rural revitalization; Rural aging human resources; Digital intelligence; DST development model (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-699-9_17

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