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.

Individuals with high career adaptability tend to navigate career transitions more smoothly. To address the challenges associated with pre-service teachers transitioning from students to teachers, it is essential to explore the development of their career adaptability. This study aims to investigate the growth of four dimensions of career adaptability in pre-service teachers—career concern, career control, career curiosity, and career confidence—over a semester, as well as how internship experiences and emotion regulation strategies (specifically cognitive reappraisal and expressive suppression) influence this growth. Three surveys were conducted over the semester, involving 779, 683, and 448 pre-service teachers, respectively, who completed questionnaires assessing career adaptability three times and emotion regulation strategies once. The results of the latent growth model showed significant increases in career concern, control, and curiosity over time, whereas career confidence exhibited a gradual decline. Both internship experiences and emotion regulation strategies predicted different aspects of career adaptability. The findings provide empirical evidence for optimizing teacher education programs, ultimately aiming to enhance the overall career adaptability of pre-service teachers.

Doctoral student agency is increasingly regarded as a key construct in doctoral education. Yet, existing research on this topic focuses on qualitative approaches, and there remains a lack of psychometrically validated instruments, particularly in the Chinese context, where supervisory authority and institutional structures strongly shape student experiences. This study aimed to develop and validate the Doctoral Student Agency Scale (DSAS) to provide a comprehensive measure of doctoral students’ agency during the process of professional socialization. A sequential mixed-methods design was adopted. First, a conceptual model was inductively constructed from semi-structured interviews with 27 doctoral students, followed by three-level qualitative coding to generate an initial pool of items. These were refined through expert review, and 436 valid responses were subjected to exploratory and confirmatory factor analyses. The final DSAS consists of 27 items organized into 7 first-order factors, which load onto 3 second-order dimensions: self-agency, academic agency, and resource agency. Moreover, DSAS scores significantly correlated with academic ability and research role identity, two critical outcomes of doctoral student professional socialization, thus confirming the criterion validity. These findings indicate that the DSAS is a valid and reliable instrument. Theoretically, it contributes to refining the multidimensional conceptualization of doctoral agency, while practically, it provides supervisors and institutions with a diagnostic tool to design targeted interventions and foster doctoral development in context-sensitive ways.

The development and application of artificial intelligence (AI) has accelerated the process of education and teaching transformation. Although prior studies have examined the forms of integrating AI into education, insights into the effective factors impacting pre-service teachers’ AI-assisted instruction intention (AI-AII) are rather limited. Considering this gap, this study constructed a structural model among AI-AII, AI pedagogical content knowledge (AI-PCK), AI technological knowledge (AI-TK), performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). This model explains how teachers can use pedagogical and content knowledge to improve instructional practices, and how technology can support the implementation of the pedagogical and content knowledge. Data were collected from 1,391 pre-service teachers in China. Results of the modelling effort indicate that the pre-service teachers’ AI-PCK, EE, PE, SI and FC positively predict their AI-AII. However, pre-service teachers’ AI-TK did not exert effects on their AI-AII. These insights are important for educators and policymakers to consider in designing teacher education and professional development related to foster pre-service teachers’ behavioural intention to use AI in teaching.

Artificial intelligence (AI) in education has attracted increasing global attention. Existing studies have mainly examined teachers’ AI adoption intention in relation to technology-related perceptions and broader school-level conditions, but the role of transformational leadership remains underexplored. Drawing on complementary perspectives from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Self-Determination Theory (SDT), this study examined transformational leadership in relation to teachers’ readiness for AI integration using complementary variable- and person-centered analyses of data from 596 Chinese in-service K-12 teachers. The variable-centered results showed that transformational leadership was positively associated with teachers’ behavioral intention to use AI tools, with indirect associations involving willingness to develop AI competence and constructivist teaching beliefs. The person-centered analysis identified four readiness profiles: (1) Favorable Orientation but Behavioral Hesitation, (2) Limited Orientation and Behavioral Hesitation, (3) Low Orientation and Weak Behavioral Inclination, and (4) Favorable Orientation and Behavioral Inclination. Profiles 1 and 4 both showed relatively high constructivist beliefs and willingness to develop AI competence, but Profile 4 reported stronger behavioral intention to use AI tools. Profiles 2 and 3 were below the sample average across all three indicators, with Profile 3 showing the lowest behavioral intention. Relative to Profile 1, higher transformational leadership was associated with greater odds of membership in Profile 4 and lower odds of membership in Profile 3. These findings suggest that teachers’ readiness for AI integration varies in both level and configuration, highlighting the need for differentiated leadership and professional support.