Teacher Optimism on AI Is Dropping. The Reason Isn’t the Technology.
What research reveals about the absence of training, policy, and institutional support
Teacher Optimism on AI Is Dropping. The Reason Isn’t the Technology.
Sponsored by Overdeck Family Foundation
By: Lin Ler
Lin Ler is a Stanford MBA and MA in Education candidate working at the intersection of business strategy, learning science, and frontier AI. With a background spanning management consulting, EdTech startups, and the social sector, Lin translates complex AI capabilities into practical insight for the future of learning.
Spend some time on Linkedin these days and you’re likely to see polarized views about AI… and perspectives from educators are no exception. For every educator bursting with excitement about the innovative AI tools and simulations they’re building for their students, there is another voicing concern that AI has been eroding their students’ ability to think critically and wrestling with how to change their assessment strategy in the face of AI. Just this week, Mississippi college instructor Jason Gibson became an educator folk hero for embedding a clever trap into an midterm assessment that caught more than 90% of his students using AI.
So what does the research actually say about educator’s responses to AI? One research team from the University of Washington analyzed more than 13,000 unscripted conversations between K-12 educators and AI on an open-access platform. What they saw is teachers treating AI primarily as a “thought partner” or assistant that executes teacher-defined goals, rather than as a coach meant to improve their own instructional practices. Teachers leaned on it to reduce workload and increase efficiency — lesson planning, generating assessments, curating teaching materials, and creating differentiated or bilingual content tailored to diverse student needs.
But as you might expect, teachers are also critical about how AI is used in the classroom, by both educators and students. A Stanford team that convened teachers for a Practitioner Voices Summit found that educators evaluate AI tools through what the researchers call “deliberative sensemaking” — weighing productive tensions, like balancing the desire for personalized learning against concerns over fairness and bias, or weighing the adaptability of a tool against the time it takes to use it efficiently. Their primary concerns revolve around inaccuracies, generic outputs, and the risk of academic dishonesty. A study of 167 preservice teachers across four teacher education institutions in Ghana surfaced a different worry: that AI lacks the capacity for emotional connection and personalized human support, potentially weakening the student-teacher relationship. And a nationwide survey of Brazilian teachers raises the concern that, much like previous educational technologies, AI might exacerbate existing inequalities if introduced without careful consideration of diverse classroom contexts and resources.
The Souring of Educator Attitudes
While there is still diverse sentiments among teachers, polling has indicated a measurable cooling trend in teachers’ enthusiasm for AI. A 2026 spring survey revealed that a majority (55%) of teachers now oppose using AI in the classroom, representing an 8-point drop in support since the beginning of the school year. Furthermore, 65% of teachers oppose allowing their students to use AI for schoolwork, which is also an 8-point increase in opposition. This trend is mirrored in higher education, where faculty intent to use AI has declined significantly in the U.S. and Canada. And the Brazilian teachers surveyed earlier voiced a similar mix of worries: generic outputs, overreliance on technology, and students misusing AI for an unfair advantage.
Importantly, this cooling is not indifference — it is coming from a workforce that takes AI seriously. A nationally representative NPR/Ipsos poll of 545 K-12 teachers found that nearly 3-in-4 believe AI has bigger implications for education than past innovations like the internet or computers, and 60% are already using it themselves to save time and improve their teaching materials. Those same teachers are the ones sounding the alarm: 54% say AI makes it harder for students to develop critical thinking skills, 55% see it as mostly a shortcut for students to avoid doing the work, and nearly 6-in-10 say it is eroding trust between students and teachers. The skepticism is a considered judgment from heavy users, not a reflex from the sidelines.
It is tempting to read this resistance as generational, assuming that veteran teachers are rejecting a cutting edge technology they didn’t grow up with. The data says otherwise. When a research team surveyed 508 K-12 teachers across six countries — Brazil, Israel, Japan, Norway, Sweden, and the United States — they found that age and education level had negligible effect on whether teachers trust AI. What predicted trust was self-efficacy: a teacher’s confidence in their own ability to understand and use these systems. Teachers with higher self-efficacy perceived more benefits, fewer concerns, and reported more trust. The change in attitudes is not only a verdict on the technology, but also partially a signal about the lack of support teachers are receiving.
The Policy Vacuum and the Burden of Ambiguity
It’s important to note that teacher skepticism is often exacerbated by the fact that schools are failing to provide a roadmap for implementing AI. Only 18% of U.S. K-12 teachers report receiving formal, written guidance on how to use AI tools at work, according to a 2026 Walton Family Foundation–Gallup survey of 2,069 public school teachers. For highly consequential tasks, the void is even larger: 69% of teachers receive absolutely no guidance on using AI for one-on-one student tutoring, and 58% receive no guidance on using it for grading and providing feedback. The pattern holds across nearly every use case — fewer than one in ten teachers receive formal guidelines for any specific AI-assisted work activity, including just 5% for tutoring students, 6% for analyzing patterns in student achievement data, and 7% for getting AI coaching on their own teaching.
When formal policies are absent, the burden of navigating this rapidly changing and ethically complex technology falls entirely on individual educators. The same Walton–Gallup research links this lack of role clarity and the weight of unrealistic expectations directly to higher burnout rates and lower job engagement. The policy gap also worsens inequities, as teachers in higher-need schools are even less likely to receive guidance on AI use than those in wealthier schools.
In that vacuum, each teacher’s AI policy is assembled alone, from trial and error or through unofficial teacher channels. This is a burden on teachers and students alike; how can students successfully navigate an educational environment where every class has a different policy and a strategy that is allowed, even required, in one class might mean expulsion from another?
Global Evidence for AI Teacher Training
Educators across the globe acutely feel this lack of preparation. Returning to that Brazilian national survey, over 80% of teachers reported having only basic or limited knowledge of AI, citing a lack of training and technical support as the primary barriers to implementation. Despite these frustrations, teachers have a strong appetite for training: the same share, over 80%, said they wanted continuous, online professional development tailored to their knowledge levels.
A separate team running a national survey of teachers in Indonesia heard the same concerns. Even when teachers do receive some professional learning on educational technology, it often misses the mark on critical evaluation; for instance, while many pre-K teachers receive tech training, only about a third are taught how to actually judge the quality of those tools.
Many teachers emphasize that they do not just need technical tutorials; they need spaces where they are given protected time to collaboratively explore tools, push back against bad ideas, and align AI with their pedagogical values.
The Transformative Impact of Formal Support
The data shows that when schools do provide clear policies and training, teacher attitudes shift dramatically:
Formal guidance encourages adoption: 69% of teachers who receive formal, official guidance say it actively encourages them to use AI, compared to only 51% of those who receive informal or verbal guidance.
Training builds confidence and reduces fear: A team at the University of Cologne put nearly 300 German teachers through a four-week online AI certificate course and measured them before and after — perceived preparedness improved markedly, and the training demystified the technology, shifting perceptions from science-fiction notions of AI toward a realistic grasp of its everyday applications and limitations. A smaller pilot training science teachers in prompt engineeringfound the same direction of travel.
Diagnosis should precede training: Because self-assessment is unreliable, a team presenting at the LAK26 learning analytics conference argues for objective diagnostics first — profile who overestimates, who underestimates, and who is starting from zero, then match the intervention to the profile. The rubric for this already exists in UNESCO’s AI Competency Framework for Teachers; what most districts lack is the diagnostic step.
There is a big catch to this background context: for the most part, when teachers are asked about their own AI literacy, results are self-reported. That LAK26 team measured teachers’ AI literacy both ways — asking them to rate themselves, and testing them objectively… and the correlation between the two was low. Teachers sorted into six distinct profiles, including overestimators whose confidence outran their tested ability and underestimators whose tested ability outran their confidence. Underestimators distrust tools they could use well, while overestimators trust tools they cannot critically evaluate.
Ultimately, researchers who interviewed 22 teachers in an early-adopting U.S. district came away with a clear read: teachers are not inherently anti-technology, but they are understandably resisting being forced to implement complex, high-stakes tools in a vacuum. Schools that are willing to invest in clear policies and comprehensive training can alleviate this burnout, transforming AI from a source of anxiety into a supported, effective classroom asset.
How Schools Might Support Teachers
What schools are actually doing, then, falls short in two connected ways: they are leaving teachers without a map, and they are measuring readiness in self reporting that may be disconnected from reality.
Put the guidance in writing, because written policy measurably encourages use where verbal suggestion does not.
Protect time for teachers to make sense of these tools together, because collaborative testing is how miscalibrated confidence gets corrected.
Assess AI literacy the way schools assess every other competency they take seriously: objectively, before the training dollars are spent.
We would never accept students self-reporting their own reading levels as an assessment system. For teacher AI literacy, that is currently the assessment system, and the souring poll numbers are what it produces.
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Special thanks to Overdeck Family Foundation for sponsoring this article in our AI & Efficacy Editorial Research Series diving into key research findings from Stanford’s AI Hub for Education Research Repository (a project by Stanford’s SCALE Initiative).
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