Factors associated with the level of occupational stress among nurses-a cross-sectional study.

In contemporary educational contexts, English proficiency is increasingly recognized as a critical competency for the academic and career development of vocational students (; ). Although the impact of generative artificial intelligence (Gen AI) on university students’ English as a Foreign Language (EFL) proficiency has been extensively examined in recent reviews and empirical studies (; ), vocational college students remain a largely underexplored population (). According to a survey conducted by the UNESCO International Institute for Educational Planning, nearly one-quarter of existing occupations are expected to undergo substantial technological transformation, highlighting the urgent need for vocational education and training systems to adapt to emerging labor-market demands (). Similarly, a national survey conducted by the Center for Vocational Education Development of the Chinese Ministry of Education revealed that although 99.4% of vocational students reported using AI-related learning resources, more than 60% lacked systematic practical training (). These findings underscore the importance of investigating how Gen AI can effectively support vocational students’ language learning and professional development. In contrast to earlier artificial intelligence systems that predominantly depend on predictive models derived from historical datasets. Gen AI is capable of delivering prompt and meaningful feedback essential for language development and providing tailored recommendations (; ). In the literature for the past 5 years, there have been various applications of Gen AI, including generating realistic images and videos that help students with their learning (; ) Fostering Gen AI in English education is necessary in that a significant body of literature indicates that AI could deliver customized instructional materials (), and facilitate intelligent teaching practices (), provide students with timely feedback (). However, there are challenges posed by the limitations of GenAI, such as encouraging superficial study and relying on automated support excessively (), which compromise students’ genuine efforts to develop their language-learning competence. Empirical evidence regarding whether Gen AI directly improves motivation in the EFL context remains inconsistent, especially among vocational students. Some studies focus primarily on the impact of AI on learning outcomes (; ), while others discuss mediating factors such as learning motivation and engagement among university students (; ). In a report released by Modern Higher Vocational Colleges Technical network () which is related to China’s National Association of Vocational College Presidents, 101,233 vocational students from 203 higher vocational schools, 44.3% of vocational students reported a lack of learning motivation, which mainly comes from employment, compared with university students’ motivation of enrolling in a higher-level school and going abroad. These motivational differences suggest a need to investigate the motivating factors shaping vocational students’ language study ().