Actionable companion · Staff capability and AI literacy

From evidence to district action: Balancing beliefs: exploring preservice teachers’ generative artificial intelligence perceptions and intentions for practice

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.

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:

Generative artificial intelligence (GenAI) tools such as ChatGPT have rapidly entered educational contexts, raising questions about their affordances, limitations, and implications for teacher preparation. This qualitative, exploratory case study investigated preservice teachers’ perceptions of ChatGPT across two sites in Los Angeles and Hawai‘i, with 74 participants. Data were collected through surveys embedded in interactive learning modules and analyzed using thematic analysis. Findings indicated alignment in perceived affordances for both students and teachers, including idea generation, and the importance of knowing how to use tools properly. Participants also emphasized limitations such as risks of dependency, plagiarism, and exposure to bias or misinformation. While most preservice teachers expressed willingness to use ChatGPT in their future teaching, few supported future student use. Across responses, participants highlighted the need for training to ensure ethical and effective implementation. This study underscores the importance of integrating both technical and critical AI literacy into teacher education.

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. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. 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. Measure error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. 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

Mike Karlin; Cristina Stephany; 'Alohilani Okamura; Yoon Jin Nam-Huh. Balancing beliefs: exploring preservice teachers’ generative artificial intelligence perceptions and intentions for practice. Frontiers in Artificial Intelligence (2026-06-01). https://doi.org/10.3389/frai.2026.1834667

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.