Sustainable development of rural has become an essential global plan. Habitat sustainability assessment of rural is a critical management tool to grasp the development status of rural in real-time and enable dynamic adjustment of policies. This paper combines the 2030 Sustainable Development Goals (SDGs) with the entropy weight method, TOPSIS, and grey correlation analysis to construct a multi-criteria decision-making (MCDM) evaluation model, which is finally used to assess the sustainability of the rural human settlement environment. Finally, this paper uses the rural of 11 prefecture-level cities in Zhejiang Province in 2021 as a case study for rural human settlement environment sustainability evaluation. The results show that the overall rural human settlement environment sustainability level in Zhejiang Province is better than in most regions in China. Hangzhou has the best rural human settlement environment sustainability, and Zhoushan has the worst. In addition, the production environment factor is the critical factor that constrains sustainability. The study results provide references and guidance to policymakers for sustainable development initiatives.

In the context of global economic digital transformation and technological innovation, the application of AI Technology has a profound impact on corporate innovation and development. Existing research has primarily focused on the direct effect of AI Technology on Corporate Innovation Performance, while there is limited exploration of its interaction with organizational learning mechanisms. Based on the Dynamic Capabilities Theory, this study constructs a framework of “Technology—Individual Learning Capability—Team Learning Capability—Innovation Performance”, analyzing how AI Technology enhances learning capabilities to drive improvements in innovation performance and explores the moderating role of Organizational Learning Capability. Through empirical analysis of data from Specialized, Refined, Unique, and Innovative Enterprises in China, the study finds that AI Technology significantly enhances Corporate Innovation Performance, with Organizational Learning Capability playing a critical moderating role. Additionally, heterogeneity analysis indicates that factors such as production factors, industry characteristics, and firm size significantly influence the effectiveness of AI Technology in enhancing innovation performance. This research reveals the pathway through which AI Technology optimizes organizational learning mechanisms to improve innovation performance, offering both theoretical support and practical guidance for corporate strategic decision-making.

Intelligent education systems are reshaping the language learning ecosystem; however, the psychological mechanisms linking technology dependency (TD) and oral English anxiety (OA) remain under-explored. This study employs a mixed-methods approach grounded in Cognitive Load Theory (Sweller, 1988) and Social Cognitive Theory (Bandura, 1986), supplemented by Trait Activation Theory to examine personality moderation associations.Surveying 716 university students in Beijing, Shanghai, Guangzhou, and Chongqing, it investigates the mediating effects of psychological burden (PB) and digital self-efficacy (DSE) on the TD-OA relationship, alongside the moderating associations of personality traits. Mplus structural equation modelling was combined with Python XGBoost-SHAP machine learning. SEM revealed an asymmetric dual-path mechanism: PB acted as a complementary mediator, amplifying the positive association between TD and OA, while DSE functioned as a competitive mediator, marginally buffering the association with a path ratio of 7.83:1. Personality traits significantly modulated these relationships: Extraversion (EX) enhanced resilience to technology-induced anxiety, while Neuroticism (NEU) amplified vulnerability. Mixed-methods enhanced predictive accuracy by detecting complex nonlinear interactions. The study contributes in two key areas: 1. A dual-process model explaining technology dependence by highlighting differentiated associations through cognitive load and efficacy pathways; 2.A trait adaptation framework that clarifies how personality traits are associated with psychological adaptation in technology learning environments. Practical implications inform the development of "anxiety-sensitive" intelligent tutoring systems. This research advocates for adaptive adjustments based on psychological burden and personality-oriented personalised intervention strategies, emphasizing the importance of protecting learners' mental health while pursuing technological efficiency.