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Educators Say AI Is Everywhere In Schools With Few Rules To Guide It

In a Nutshell
  • Many surveyed educators reported occasional or frequent use of AI tools like ChatGPT for everyday tasks such as lesson planning, writing, and student support.
  • Cheating and the trouble of gauging what students actually know on their own were the top concerns across both phases.
  • More respondents reported no formal district AI policy, or weren’t sure one existed, than reported a clear policy in place.

Teachers, administrators, and counselors have quietly folded AI into the school day, mostly without a rulebook to guide them. Educators in a new survey reported using tools like ChatGPT to write lesson plans, draft parent emails, and build support materials for students with disabilities. Many said their districts still have no formal rules for any of it.

Brett DeJager, an assistant professor of school psychology at the university, ran the two-phase study, released through the University of Wisconsin–Stout’s institutional repository, MINDS@UW. Phase one focused on Wisconsin and drew 303 K–12 professionals. Phase two reached 132 more across 38 states and Washington, D.C. Most were experienced staff with more than eight years in schools, which makes their fast pickup of a brand-new technology worth a closer look.

Speed is the story here. The technology moved through educators’ daily work faster than their institutions could respond, leaving teachers, administrators, and counselors using powerful tools on their own judgment while sorting out what it all means for students and learning.

Teacher or professor laughing with students
More teachers are turning to AI platforms for help creating lesson plans. (© Robert Kneschke – stock.adobe.com)

AI in Schools Is Already Part of the Job

ChatGPT led the pack in both phases, used by 88% of Wisconsin respondents and 81% of the national sample. Respondents named plenty of others too: Google Gemini, Microsoft CoPilot, Grammarly, education-specific platforms, and assistants baked into software they already open every day.

Reported uses skewed practical rather than experimental, though these role-specific questions went only to people in each job. Among teachers who answered, common uses included creating classroom content, tailoring lessons for different learners, and building materials for students with disabilities. Administrators most often drafted emails, wrote memos, and assembled training materials. Counselors and psychologists developed mental health resources, wrote behavioral plans, and supported individualized education programs, the legally required plans that lay out services for students with disabilities.

One respondent put the appeal simply: “Love using AI for administrative tasks that take a lot of my mental bandwidth, like parent emails, report card comments, admin tasks, etc.” Another described a narrower use: “AI helps me breakdown the components of IEP goals so I can create a plan of action for students working towards the goal.”

Cheating Tops the Worry List, But the Fear Runs Deeper

Asked about their concerns, respondents put academic dishonesty and plagiarism at the top in both phases, flagged by 65% in Wisconsin and 74% nationally. Worry about cheating sat on top of a broader unease. About half in both groups said they had trouble telling what students genuinely know once AI can produce essays, explanations, and answers on demand. More than half flagged AI churning out biased or false information. Data privacy, student over-reliance, and equal access to the tools ranked high as well.

When respondents weighed AI’s effect on students, increased dishonesty again came first. Right behind it, especially in the national sample, came worries that students lean too hard on AI and lose ground on critical thinking and problem-solving. As one respondent wrote: “Students were already struggling with independence before AI and now it’s so much worse.”

For many respondents, the sharper question went past catching cheaters: could the usual ways of measuring what a student knows still hold up when a chatbot can do so much of the work?

Infographic summarizing a University of Wisconsin–Stout two-phase survey showing widespread AI use in K–12 schools, limited formal district AI policies, top educator concerns about cheating and student learning, and strong demand for free AI training.
Infographic by StudyFinds

AI in Schools Has Outpaced the Rulebook

For all that daily use, formal district rules remained the exception. In Wisconsin, 33% of respondents said their district had a formal AI policy; nationally the figure was 29%. In both phases, the most common answer was that no official policy existed or that the respondent simply didn’t know. Among administrators and IT staff, preventing student misuse and protecting student data drove most of whatever limits were in place, each cited by roughly half of that group in Wisconsin. Outright blocks were rare. Partial restrictions were the norm, reported by half of Wisconsin’s administrators and IT staff and 59% of the national group.

Educators Want Training, and They Want It Free

Uncertainty aside, appetite for learning ran high. A majority wanted future AI literacy training, 71% in Wisconsin and 63% nationally. Top requests were ethical and responsible use, AI for special education and accommodations, administrative efficiency, and folding AI into lesson planning and assessment. Format preferences split fairly evenly across in-person sessions, virtual workshops, and self-paced courses. One condition came up more than any other: the training had to be free.

Across both phases, the picture is of a workforce that got out ahead of the institutions around it, using AI widely, unsure of the rules, and eager for guidance no one has handed them yet. Several respondents said the fix is straightforward: adopt the tools and set clear expectations for teachers and students. The demand is already there. The districts have yet to meet it.


Paper Notes

Limitations

DeJager is clear that the results are descriptive and exploratory, not definitive. Phase 1 covered Wisconsin K–12 public education professionals and should not be read as representative of all Wisconsin educators or districts. Phase 2 gathered a broader national sample but was not built to be nationally representative, and the two phases cannot be merged into a single national dataset. Gaps between them may come down to differences in who responded, how they were recruited, timing, geography, or the mix of job roles. Findings about student behavior, including dishonesty, critical thinking, and mental health, reflect what respondents perceived rather than independently verified student outcomes. Written open-ended answers were reviewed for themes, not formally coded as qualitative data.

Funding and Disclosures

DeJager conducted the study as part of his faculty research at the University of Wisconsin–Stout Polytechnic. He reported no funding from AI companies, educational technology vendors, or any commercial organizations tied to the work.

Publication Details

Author: Brett DeJager, Psy.D., Assistant Professor of School Psychology, University of Wisconsin–Stout Polytechnic.

Paper title: “The Role and Impact of Generative Artificial Intelligence in K–12 Public Education: Findings from a Two-Phase Survey Study.” Institution: University of Wisconsin–Stout. Draft prepared: June 2026. IRB approval: University of Wisconsin–Stout Institutional Review Board (IRB-FY2025-130). Suggested citation, as provided in the paper: DeJager, B. (2026). The role and impact of generative artificial intelligence in K–12 public education: Findings from a two-phase survey study. University of Wisconsin–Stout. Available at the University of Wisconsin–Stout institutional repository (MINDS@UW).

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