Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers’ viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers’ behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers’ behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers’ perspectives and intentions regarding GenAI technology.

This study examines Chinese university students’ adoption of generative artificial intelligence (GenAI) tools by integrating the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and AI literacy dimensions into a hybrid framework. Survey data from 1006 students across various majors and regions are analyzed using partial least squares structural equation modeling. Notably, AI literacy (i.e., students’ AI ethics, evaluation, and awareness) positively affect their attitudes, subjective norms, and perceived behavioral control, although the influence patterns vary according to the literacy dimension. Perceived privacy risks reduce AI trust, which mediates adoption behavior. Overall, core TPB pathways are validated, with behavioral intentions significantly predicting students’ actual use. Gender and regional differences moderate the key relationships. The results of this study suggest that enhancing students’ ethical and evaluative competencies, building user trust, and addressing privacy concerns could promote generative AI integration in education.

Generative artificial intelligence (GenAI) is reshaping higher education by influencing students’ learning, cognition, and academic decision-making.

The COVID-19 pandemic has had a significant impact on secondary school teaching and learning models and has changed the way students attend classes. This study examined the impact of teacher–student relationships on secondary school students' online learning motivation during the pandemic, using selfdetermination theory. We proposed that basic psychological needs would mediate this relationship, with affective experiences playing a moderating role. Data were collected through an online survey of 1,168 secondary school students during the outbreak containment period in China (October 14–21, 2022). The results indicated that teacher–student relationships positively influenced motivation, with basic psychological needs serving as a partial mediator, highlighting the moderating role of emotional experience in the direct association between teacher–student relationships and online learning motivation. The research enhances understanding of the dynamics between teacher support and student needs in online learning environments, and has implications for promoting student engagement and educational outcomes.