Construction and validation of an evaluation index system for healthy villages in China: A human-AI synergistic approach integrating modified Delphi and AHP.

Objective: Research on healthy villages in China is currently constrained by limited evaluation criteria and a lack of systemic comprehensiveness. This study aims to develop a scientifically rigorous evaluation index system that is tailored to the regional characteristics of China. Method: A modified Delphi method was employed to screen indicators based on literature review and expert consultation, followed by the Analytic Hierarchy Process (AHP) to determine the weights of these indicators. Innovatively, this study adopted a Human-AI synergistic approach throughout the research lifecycle; generative AI was utilized to refine indicator semantics during the Delphi phase, while an LLM-assisted comparative analysis served as a robustness check for the weighting system. Additionally, empirical validation was conducted in three pilot villages. Results: The final system consists of 7 first-level, 31 s-level, and 61 third-level indicators. Metrics from expert consultations were satisfactory, with authority coefficients exceeding 0.80 and demonstrating strong coordination ( Conclusion: The constructed index system integrates multi-dimensional health factors with a scientifically rigorous design validated through this Human-AI synergistic strategy. Ultimately, this approach pioneers new pathways for the deep integration of artificial intelligence and public health management, while providing a reference model for developing comprehensive evaluation systems in other developing countries.