From leadership research to district action: Transformational Leadership and Heterogeneity in Teachers’ Readiness for AI Integration: A Complementary Variable- and Person-Centered Approach

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract 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. Keywords: AI tools, behavioral intention, readiness for AI integration, UTAUT2, self-determination theory

Study methods

Data were collected in October 2025 through the Wenjuanxing online survey platform among K-12 teachers in mainland China. A convenience sampling approach was adopted. Participation was voluntary and anonymous. A total of 626 questionnaires were returned. Responses completed in less than 120 s were excluded. The cutoff was established based on pilot testing and an estimate of the minimum plausible time required to read and respond to the demographic items and the 29 focal scale items with adequate attention. Cases exhibiting straightlining patterns or insufficient response variability across questionnaire items were also excluded. After excluding 30 invalid responses based on these criteria, 596 valid responses were retained for analysis.

Limitations

Despite its theoretical and practical contributions, this study has several limitations. One limitation concerns the cross-sectional and self-reported nature of the data. The cross-sectional design limits the interpretation of the structural associations. Because TL, CT, TW, and BI were measured at the same time, the findings do not establish causal relations or the temporal ordering implied by the specified indirect associations. Longitudinal studies are needed to examine whether these associations persist over time and whether the proposed ordering is supported across measurement occasions. In addition, all focal variables were assessed through teacher self-reports collected at the same time. Although the common-method diagnostics did not support a single-factor representation of the item covariance, common method variance and social desirability effects cannot be entirely ruled out. Future research could reduce this concern through temporally separated measurement and multiple data sources, such as school leaders, classroom observations, or behavioral indicators.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Choose one school improvement question with teachers. Protect a short shared planning cycle, agree an instructional change and use a common rubric for examining student work. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Review implementation fidelity, teacher time and independently scored student work against a recorded baseline. Examine access differences and competing explanations before attributing changes to leadership. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Yaxuan Geng; Kangjie Geng; Kai Wang; Qianqian Ruan. Transformational Leadership and Heterogeneity in Teachers’ Readiness for AI Integration: A Complementary Variable- and Person-Centered Approach. Behavioral Sciences (2026-10-13). https://doi.org/10.3390/bs16101892

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.