The rapid development of generative artificial intelligence (GenAI) technology has triggered extensive discussions about its potential applications in sustainable higher education. Based on the technology acceptance model (TAM) and task–technology fit (TTF) theory, this research aimed to investigate the current situations and challenges of Chinese university students using GenAI in four typical task scenarios. This was performed using a cross-sectional research design. The data were collected via questionnaire, with 486 undergraduates from a Chinese university participating. The data analysis methods include descriptive statistics, inferential statistics, and content analysis. The results show that more than 70% of university students actively use GenAI, but nearly half of them are not very proficient in its use. Doubao and ERNIE Bot are the GenAI tools they prefer most. The primary functions they use are text production and information retrieval. They mainly learn the relevant knowledge and skills through self-media and knowledge-sharing platforms. Among the four typical task scenarios, GenAI is widely used in course learning and research activities, while its application in daily life and job search is relatively limited. The analysis of demographic variables shows that grade and major have a significant impact on university students’ use of GenAI. In addition, university students suggest that universities should offer relevant courses or lectures and provide comprehensive technical support to improve the popularity and operability of GenAI. This study provides suggestions for universities, education administration departments, and technology development departments to improve GenAI services. It will help universities optimize the allocation of educational resources and promote educational equity for sustainability.

The benefits of online homework are widely acknowledged for student learning. Numerous studies have provided evidence supporting the notion that the completion of an adequate amount of traditional homework is associated with enhanced academic success. However, there are inconsistent conclusions on the effect of homework completion on academic performance among the students who were exposed to online homework, particularly in China. In order to explore the behaviors of Chinese secondary students towards online homework, a correlational analysis is employed to determine the relationship between homework completion and academic performance among 103 students from China. Findings suggest a positive yet moderate association between online homework completion and academic performance. A follow-up qualitative interview with 10 purposefully selected students, based on the UX model by Topolewski, Krawczyk, Pallot & Huotari (2020), reveals that the cognitive and emotional factors are most influential on their behaviors towards online homework. Further studies are recommended to delve into the factors influencing students' intention to engage with online homework. Additionally, limitations of this study are outlined.

The emergence of online homework systems supplements the requirement of online education for learning consolidation. However, there is a lack of exploration of intention to use this facilitator for English learning at the secondary education level of Chinese students. Hence, this study proposed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with interest and hedonic motivation to explore the determinants behind the intention to embrace online homework systems for English learning. Data were randomly collected from 277 out of 1,466 Chinese secondary school students via validated questionnaires. Correlational analyses showed that five out of six postulated variables were significantly related to students' behavioral intention when using an online homework system for learning. Positive and significant associations existed between interest, facilitating conditions, performance expectancy, hedonic motivation, social influence and behavioral intention. However, there was a nonsignificant relationship between effort expectancy and behavioral intention. Additionally, interest was found to be the strongest factor related to the acceptance of the system among students. Enhancing interest and hedonic motivation, providing sufficient facilitating conditions and social support, as well as customizing reachable performance expectancy may promote the acceptance of online homework systems for English learning among Chinese secondary school students.