Actionable companion · Staff capability and AI literacy
From evidence to district action: Addicted to AI? Prevalence and psychological drivers of generative AI dependency among pre-service teachers
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:
Introduction Generative artificial intelligence (GenAI) has become embedded in higher education, raising concerns about students’ behavioral dependency on these tools. Focusing on pre-service teachers—who will shape how a future generation learns to use AI—we asked how prevalent GenAI dependency is, what drives it, and whether perceived AI literacy protects against it. Methods In a mixed-methods design, 565 education-major students in Chinese teacher-preparation programs completed an online survey, and five were interviewed. Results Dependency markers were already widespread: 79% called AI indispensable to their learning, 78% felt unaccustomed without it, 54% “asked AI first,” and 31% used its answers without verification. In the structural model (R2 = 0.60), the strongest drivers were impulsivity (β = 0.40) and anthropomorphic attachment (β = 0.28), with a smaller contribution from AI anxiety (β = 0.15); trust was non-significant. Necessary condition analysis identified social norms as a necessary-in-degree condition. Perceived AI literacy was modestly protective (β = −0.19), stronger in the full model than its zero-order correlation (r = −0.14)—a suppression effect, because more AI-literate students also trusted AI more and sat within more AI-reliant peer groups. Interviews converged with the survey model, most strikingly on anthropomorphic attachment, while qualifying the smaller anxiety pathway. Discussion Dependency is driven chiefly by dispositional impulsivity and affective attachment rather than by low competence; interventions—for policymakers, teacher educators, and pre-service teachers—should prioritize self-regulation and awareness of parasocial engagement, treating AI-literacy training as a secondary lever.
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Bin Jiang; Zhuo Wang. Addicted to AI? Prevalence and psychological drivers of generative AI dependency among pre-service teachers. Frontiers in Psychology (2026-08-12). https://doi.org/10.3389/fpsyg.2026.1922716
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.