Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Teachers’ competence, self-efficacy, and their attitudes toward generative AI in education: a correlational study

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background The rapid integration of Generative Artificial Intelligence (GenAI) offers new opportunities and challenges for education, with teacher adoption being a key factor for successful implementation. However, the psychological factors influencing teachers’ attitudes toward these tools remain poorly understood. This study explores the relationship between K-12 teachers’ digital competence, their self-efficacy for AI integration, and their attitudes toward using GenAI in education. Methods A quantitative, cross-sectional survey design was used. Data were collected from 352 K-12 teachers in a large urban school district in Indonesia through an online questionnaire. The survey assessed four constructs: General Digital Competence (GDC), AI-Specific Competence (AISC), Teacher Self-Efficacy for AI Integration (TSE-AI), and Attitudes Toward Generative AI in Education (ATGAI-E). Data analysis involved Pearson’s correlations and hierarchical multiple regression. Results The analysis showed strong positive correlations among all variables. Attitudes toward GenAI had the strongest correlation with TSE-AI (r = 0.75, p < 0.001), followed by AISC (r = 0.69, p < 0.001) and GDC (r = 0.58, p < 0.001). The hierarchical regression model explained 62% of the variance in teachers’ attitudes (Adjusted R2 = 0.61). After accounting for demographic factors, TSE-AI was the most significant predictor (β = 0.48, p < 0.001), with AISC (β = 0.25, p < 0.001) and GDC (β = 0.11, p = 0.015) also contributing. Discussion The findings indicate that while both general and AI-specific skills are important, a teacher’s self-efficacy, their confidence in their ability to use GenAI effectively, is the most powerful predictor of their attitude. This suggests that successful GenAI adoption depends more on psychological empowerment than on technical training alone. Professional development programs should therefore focus on building teachers’ confidence through hands-on practice, peer modeling, and supportive feedback to encourage positive engagement with the GenAI classroom. Keywords: digital competence, educational technology, generative AI, self-efficacy, teacher attitudes

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Emmanuel Nana Kwesi Ofori Darko; Zhanyong Qi; Zhiyuan Wang. Teachers’ competence, self-efficacy, and their attitudes toward generative AI in education: a correlational study. Frontiers in Psychology (2026-05-26). https://doi.org/10.3389/fpsyg.2026.1756148

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.