In the context of the in-depth implimentation of the “Digital China” strategy, artificial intelligence (AI) has increasingly become the core engine to drive the high-quality development of tourism. However, at present, the integration of AI and tourism still faces multiple challenges such as the absence of technical ethics, data security risks, and insufficient institutional guarantees, which need to be systematically sorted out and addressed. This paper focuses on the key issues of AI-empowered tourism industry, and systematically analyzes its path mechanism and practical problems in tourism product innovation, service reconstruction, and governance system upgrading. The findings are as follows: (1) AI technology reshapes the operating logic of the tourism industry, promotes the transformation of the industry from element-driven to intelligence-driven, and builds a “data-algorithm-service” loop; (2) As a new production factor, AI is deeply embedded in the function of tourism production, enabling labor enhancement, capital optimization and intelligently resource scheduling through technology; (3) AI drives the integration of culture and tourism into a new stage characterized by digitized resources, immersed experiences, personalized supply and intelligent decision-making, and giving rise to diverse integrated business formats; (4) AI helps build a “government-enterprise-community-tourists” collaborative governance network to promote the transformation of tourism destination governance into an intelligent ecosystem; (5) The risks and challenges in the development of AI are becoming increasingly prominent, and it is necessary to make multidimensional efforts from institutional construction, technical supervision, ethical governance and personnel training to build an inclusive, safe and fair technical governance system. This study helps to clarify the core logic of AI-driven tourism industry transformation, and provides theoretical support and policy suggestions for building a new tourism development model characterized by intelligent co-creation.

The reconstruction of the tourism discipline driven by artificial intelligence (AI) is a core issue in aligning tourism education with China’s national strategy of building a strong education system and supporting the intelligent transformation of the cultural and tourism industries. However, systematic research on how AI can be deeply integrated into the development of the tourism discipline remains limited. This underscores a knowledge gap and the urgent need to integrate AI into tourism education. This paper explores the emerging opportunities, challenges, and paradigms in the AI-driven reconstruction of tourism education. The findings reveal that: (1) AI is reshaping the theoretical foundations of the tourism discipline, fostering interdisciplinary integration and knowledge system reconstruction, thereby catalyzing novel theoretical paradigms that extend tourism research boundaries. (2) AI technologies are expediting the integration of AI into educational practices, leading to profound transformations in curriculum design, instructional methods, and assessment frameworks, in turn redefining teaching roles and enabling more personalized learning experiences. (3) Tourism discipline faces multiple challenges during this transformation, including cognitive misalignment, difficulties in technological implementation, and lagging ethical governance, highlighting the need for unified understanding, robust support for implementation, and updated ethical guidelines. (4) Future discipline development should prioritize technological governance, institutional innovation, and cultural guidance to facilitate intelligent advancement and value recreation in tourism education, ensuring responsible AI adoption, institutional adaptability, and preservation of core cultural values. This study enriches framework for tourism discipline development in the AI era from the theoretical and practical perspective and offers actionable policy recommendations for decisionmakers and educational administrators, contributing to the modernization of a tourism discipline system with Chinese characteristics.

Artificial Intelligence (AI), as a representative technology of the fourth technological revolution, has become a significant driver for promoting Tourism Total Factor Productivity (TTFP) advancement. The study employs the Malmquist index model to measure tourism industry TFP across 30 Chinese provinces from 2011 to 2024, and combines the methods such as Lorenz curves and Getis-Ord Gi* statistical index to compare regional differences in AI technology and explore its impact on TTFP. The results indicate: (1) both AI technology and tourism TFP showed growth trends during 2011-2024, but with notable spatial disparities with AI development in the middle and lower reaches of the Yangtze River and southeastern coastal areas being significantly stronger than the national average, while TTFP in central and northeastern regions is higher than that in eastern and western regions. (2) AI technology has a significant positive impact on tourism technological progress and TTFP enhancement, but its effect on technical efficiency is not evident, but the quantile regression results show that the marginal utility of AI on TTFP first decreases then increases. (3) Regional heterogeneity analysis reveals that eastern, central, and northeastern regions display positive correlations between AI technology and tourism TFP, while effects in western regions have yet to appear. For future development, the tourism industry should: leverage AI technology to develop AI-Generated Content (AIGC) scenarios and enhance interactive experiences with emphasis on “emotional” and “humanistic” orientations; draw from successful experiences in other fields by establishing user profiles and constructing digital twin scenic areas to break low-level cycles; strengthen regional cooperation to promote talent mobility and technology transfer, thereby narrowing regional development gaps and improving TTFP.

Digital economy serves as a powerful driving force for the green development of cultural and tourism industries. Exploring the relationship between digital economy and green technological progress can provide guidance for the sustainable development of cultural and tourism industries. This study takes the panel data of 30 provinces in China from 2013 to 2021 as a sample to analyze the impact mechanism of digital economy on green technological progress in China’s cultural and tourism industries. The results show that: (1) during the study period, the Digital Economy Development Index and the Green Technological Progress Index of the Cultural and Tourism Industries exhibited an overall upward trajectory. However, the digital divide continued to widen, while the Green Technological Progress Index of the Cultural and Tourism Industries is significantly influenced by external environmental shocks; (2) The digital economy exerts a significant and sustained promoting effect on the green technological progress of the cultural and tourism industries; (3) The digital economy promotes the green technological progress of cultural and tourism industries by enhancing the activeness of tourism market entities and increasing the labor input in the tertiary industry; (4) The development level of digital economy and the level of labor skills have heterogeneous impacts on the green technological progress of cultural and tourism industries, and this promoting effect is more obvious in regions with backward digital economy and low labor skill levels. Based on this, the study puts forward relevant suggestions for promoting the green development of cultural and tourism industries from the aspects of giving play to the characteristics of digital economy, strengthening talent training, stimulating the vitality of cultural and tourism markets, and implementing differentiated development strategies.

Cities are important tourist destinations, and smart city construction profoundly affects tourism development. Focusing on 100 prefecture-level pilot smart cities in China, the research employs data from the CEIC database and statistical yearbooks of those prefecture-level cities from 2006-2019 to analyze the impact of smart city construction on tourism development level through difference-in-differences model and panel data regression model. Research has found that: (1) smart city construction has significantly improved tourism development level. (2) Smart city construction mainly promotes the improvement of tourism development level from four dimensions, namely, smart infrastructure, smart transportation, technological innovation, and smart economy, among them, technological innovation dimension shows the strongest influence. (3) The impact of the four dimensions of smart city construction on the tourism development level varies according to the size of the city. The dimensions of technological innovation, smart infrastructure, and smart economy have the most prominent impacts in large cities, medium-sized cities, and small cities, respectively. These findings provide empirical evidence and theoretical support for optimizing smart city initiatives to drive high-quality tourism development.

This study investigates the New Quality Productive Forces (NQPF) and tourism industrial competitiveness across 272 Chinese cities based on the entropy weight TOPSIS method to measure the coupling degree and the coordination degree between new quality productivity and tourism industrial competitiveness, and to explore the dynamic mechanism of the coordination degree using the panel data. The findings reveal that: (1) the level of NQPF is lower than the level of tourism industrial competitiveness, but the growth of the former is higher than that of the latter. Significant disparities exist among cities, with the eastern region leading in both NQPF and tourism industrial competitiveness, though growth trends remain similar across regions. (2) The overall level of coupling and coupling coordination between NQPF and tourism industrial competitiveness is moderate but shows an increasing trend year by year. There is no significant difference in the level of coupling between eastern, central and western cities, but the coupling coordination degree of eastern cities is higher than that of central and western cities. (3) Digital innovation, fiscal expenditure, urbanization, marketization, and resident consumption are the fundamental drivers for the coupled and coordinated development of new quality productivity and tourism industrial competitiveness. Digital innovation is the core driving mechanism for the coordinated development of new quality productivity and tourism industrial competitiveness in eastern, central and western cities, with eastern cities relying mainly on market power, central cities relying on the dual power of government and market, and western cities being driven by both government power and market power. In addition, high-innovation cities rely on endogenous and market dynamics, low-innovation cities are significantly influenced by exogenous dynamics, while tourism-dependent cities have a single driving mechanism, and non-tourism-dependent cities show diversified driving characteristics. These findings expand application scenarios of the theoretical framework of NQPF, deepen the understanding of its interplay with tourism development, and provide references for the synergistic enhancement of the competitiveness of new quality productivity and tourism as well as the promotion of the construction of the emerging strategic pillar industry of tourism.

The digital transformation of the tourism industry has changed the operational mode of tourist destinations and provided local residents the opportunities to participate in tourism and challenges to their pro-tourism behaviors Based on the Ability-Motivation-Opportunity (AMO) theory, this study employs Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze 297 questionnaires from residents of Pingyao Ancient City, revealing the impacts of digital literacy (digital technology, digital cognition, digital social emotion) and perceptions of economic, environmental, and socio-cultural benefits on pro-tourism behavior in configuration path analysis. The study finds that:(1) no single factor constitutes a necessary condition for pro-tourism behavior, with environmental benefit perception being the first bottleneck factor. (2) Pro-tourism behavior exhibits “multiple concurrency” and “different means but same result” effects, with four high pro-tourism behavior patterns (pure digital-driven, technology-economic-emotion, digital-economic, and comprehensive types) and four non-high pro-tourism behavior patterns (environment-emotion constrained, environment constrained, digital technology deficient, and digital social emotion insufficient types). (3) The antecedent conditions influencing high and non-high pro-tourism behavior have heterogeneity and certain substitutability. The study clarifies the multiple antecedent relationships, causal asymmetry, and multiple equivalent pathways of residents’ pro-tourism behavior, providing theoretical support for the sustainable development of tourism destinations and practical guidance for policymaking and resource allocation.