Artificial intelligence (AI) technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept (WTA) AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy (EE), performance expectancy (PE), and social influence (SI) were all positively related to college students' WTA AI-assisted learning environments. Psychological risk (PR) significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.

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During the COVID-19 pandemic, as offline learning activities were blocked, teachers’ training activities were moved from face-to-face to online training. Therefore, teachers had to join an increasing number of online training sessions. However, few studies have focused on teachers’ satisfaction with online training. To address this gap, based on the American user satisfaction theory model (ACSI), this study established the factors of expectation of online training quality, perceived online training quality, perceived online training value, and teacher satisfaction with online learning, and aimed to explore their relationships with six hypotheses. A total of 397 middle school teachers who had online training experience participated in the survey through an online questionnaire. SPSS 26.0 and AMOS 23.0 were used to analyze the data. The results showed that (1) expectation of online training quality was positively correlated with perceived online training quality; (2) expectation of online training quality was negatively correlated with perceived online training value; (3) perceived online training quality was positively correlated with perceived online training value; and (4) perceived online training value was positively correlated with online training satisfaction. The findings imply that teachers should be informed in advance of various difficulties that may be encountered in online training, so as to reduce their expectations of online training quality. In addition, in order to improve teachers’ perceived quality and perceived value of online training, intervention strategies should be proposed, online training platforms should be optimized, and online training methods should be innovated to improve teachers’ sustainable development ability.

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