Huan Song

AI-mediated teaching requires teachers to evaluate uncertain information, respond to changing conditions, and integrate AI with pedagogical judgment. This study examined four cognitive-professional resource domains relevant to intelligent professional action: digital literacy, digital resilience, AI-mediated epistemic agency (AMEA), and AI-TPACK. Variable-centered and person-centered analyses drew on a cross-sectional survey of 942 YouShi Plan preservice teachers at one Chinese university. Perceived structured teacher-education support was positively associated with all four resources. Self-reported teaching innovation competence was positively associated with jointly higher digital literacy and digital resilience, with the response surface primarily reflecting their combined level. Four resource profiles mainly differed in overall level and showed graded differences in perceived teaching innovation competence. Higher perceived support was also associated with membership in higher-resource profiles. Together, these findings connect perceived teacher-education support, cognitive-professional resource configurations, and self-reported teaching innovation competence, positioning the latter as a domain-specific perceived capability relevant to intelligent professional action in AI-mediated teaching.

Preparing preservice teachers for AI‐enabled teaching requires understanding anxiety alongside perceived capability and self‐regulated learning (SRL). Drawing on data from 12,147 third‐year preservice teachers across 17 Chinese institutions, this study combined structural equation modelling, observed‐score moderation, and latent profile analysis. Four profiles emerged: the Moderate Profile, the Lower‐Efficacy, Lower‐SRL Profile, the Low‐Anxiety, Higher‐SRL Profile, and the High‐Anxiety, Higher‐SRL Profile. The two higher‐SRL profiles shared high ICT self‐efficacy despite contrasting anxiety levels. AI anxiety was positively associated with ICT self‐efficacy, which was strongly associated with SRL. Perceived institutional ICT support strengthened the anxiety–efficacy association. These complementary findings identify perceived capability as a key differentiator of regulated learning across anxiety levels. Teacher‐education programmes can assess anxiety, efficacy, and SRL together and provide practice opportunities and institutional resources that support learning.

Enhancing preservice teachers’ commitment to teaching is of great significance for improving the stability and sustainable development of the teaching workforce. Teacher education courses constitute a core component of preservice teacher preparation, and preservice teachers’ learning experiences in these courses may be closely associated with their commitment to teaching. Based on survey data from 26,110 preservice teachers at local normal universities in China, this study treated preservice teachers’ learning experiences in teacher education courses as the focal independent variable and commitment to teaching as the dependent variable. Latent profile analysis was used to identify profiles of course learning experiences, and between-profile differences in commitment to teaching were then examined. Teacher self-efficacy was further tested as a mediator in the association between course learning experiences and commitment to teaching within each profile. Three profiles were identified: a comprehensively positive profile, characterized by high scores across good teaching, clear course goals, appropriate assessment, and appropriate workload; a mixed-experience profile, characterized by relatively positive perceptions of good teaching and clear course goals but lower evaluations of assessment and workload; and a moderate-experience profile, characterized by moderate scores across the four dimensions. Significant differences were found in commitment to teaching across the three profiles. Preservice teachers in the comprehensively positive profile reported the highest level of commitment to teaching, followed by those in the mixed-experience profile, while those in the moderate-experience profile reported the lowest level. In addition, mediation analysis showed that teacher self-efficacy played different roles across profiles. Based on these findings, this study proposes directions for reforming teacher education courses, with the aim of improving the quality of preservice teacher preparation and fostering preservice teachers’ commitment to teaching.

Digital-intelligence transformation in education has made pre-service teachers’ Technological Pedagogical Content Knowledge (TPACK) a strategic concern in teacher preparation. Survey data from 11,818 pre-service teachers across 17 local normal universities in China were analyzed through hierarchical regression, quantile regression, and structural equation modeling to examine how perceived university ICT support and perceived AI support in education are associated with self-reported TPACK. Both forms of support showed significant direct and model-conform indirect associations with self-reported TPACK, but the quantile coefficients varied across the TPACK distribution: university ICT support showed a modestly fluctuating descriptive pattern, whereas AI support in education peaked at the median and attenuated at upper quantiles. ICT self-efficacy and AI competency expectancy each formed significant indirect pathways in the hypothesized model, although the ICT pathway was more strongly indirect and the AI pathway remained more strongly direct. Additional checks of university-level ICCs, cluster-robust standard errors, and measurement invariance across key subgroups supported the robustness and comparability of the findings. These patterns clarify how perceived ICT and AI support are differentially associated with self-reported TPACK and provide empirical grounds for more precise, human-in-the-loop support designs in teacher education.

The advent of generative AI (GenAI) and its growing use in education has sparked a renewed wave of school digital transformation. School principals are pivotal in advancing and shaping school digital transformation, yet little is known about how they understand and lead digital transformation in the age of GenAI, particularly within China’s complex educational system. This study employed Q methodology to identify the perceptions and leadership styles of Chinese K–12 school principals toward school digital transformation in the age of GenAI. An analysis of a 30-item Q set with a P sample of 23 principals revealed four leadership types: Cautious Observation–Technological Gatekeeping Leadership, Moderate Ambition–Culturally Transformative Leadership, Moderate Ambition–Emotionally Empowering Leadership, and High Aspiration–Strategy-Driven Leadership. Overall, principals’ stances on GenAI formed a continuum, ranging from cautious observation and skeptical optimism to active embrace. These perceptions and leadership styles were shaped by Confucian cultural values, a flexible central–local governance arrangement, and parents’ high expectations for students’ academic achievement. Furthermore, structural constraints in resource provision further heightened principals’ reliance on maintaining guanxi-based relationships. This study enhances the understanding of the diversity of principals’ leadership practices worldwide and offers actionable insights for governments and principals to more effectively advance AI-enabled school digital transformation.

The excessive academic burden that undermines students' well‐being has become a matter of global concern. Drawing on Archer's social realist theory, this qualitative case study identified three forms of teacher agency in navigating academic burden under the ‘Double Reduction’ policy: methodological refinement that rendered the workload more engaging and sustainable; epistemological reconfiguration that transformed burden through recognition and inclusive participation; and ontological reconstruction that reframed burden as an opportunity for deep intellectual inquiry. Teachers emerged as culturally embedded, reflexive agents who reconciled institutional pressures with moral commitments. This agency challenges the compliance–resistance binary and emphasises the importance of teacher‐informed, context‐sensitive policy design.