Most of the research I cover on this blog comes from controlled experiments, lab studies, or small qualitative projects. They’re valuable, but they rarely tell us what’s happening across an entire education system. The RAND Corporation’s report on AI use in K-12 classrooms fills that gap.
Diliberti, Schwartz, Doan, Shapiro, Rainey, and Lake (2024) surveyed more than 1,000 teachers and over 200 school districts in fall 2023, then followed up with interviews with district leaders. The result is one of the first national snapshots of how American teachers were engaging with generative AI at that point.
I should say upfront: fall 2023 already feels like a different era in AI terms. The tools have changed, adoption has accelerated, and the policy conversation has matured considerably since then. But this report still matters because it gives us a baseline. It tells us where K-12 education stood roughly a year after ChatGPT’s release, and many of the patterns it documents, especially around equity and training, are almost certainly still with us.
What the National Survey Found
In fall 2023, 18 percent of teachers reported using AI tools in their teaching. Another 15 percent had tried them at least once. Most teachers had not yet used AI professionally, though adoption was clearly beginning to grow. Among teachers who were already using AI, 73 percent expected to increase their use during the following school year.
The survey provides a baseline from the first year after ChatGPT became publicly available. It cannot tell us how many teachers are using AI now. Its value lies in showing where adoption began, which teachers had access to training, and how school and district conditions shaped early use.
Three findings deserve particular attention. AI use varied across subjects and grade levels. Districts were beginning to support experimentation and professional development. Access to that support was distributed unevenly, with teachers in historically advantaged districts considerably more likely to receive AI training.
Who Was Using AI and Why It Matters
The adoption patterns are worth paying attention to, because they likely haven’t changed as much as the overall numbers. English language arts and social studies teachers were more likely to use AI than those in elementary or STEM roles. The authors suggest a practical explanation: these teachers already build and adapt their own materials regularly. If you’re someone who customizes lesson plans, adjusts reading levels, and creates your own assessments, AI fits naturally as a drafting partner. The workflow already exists. AI accelerates it.
I think there’s something deeper here too. Teachers who routinely create original content are already comfortable with iterative work. They draft, revise, and adapt constantly. That mindset aligns well with how AI works best in education: as a starting point, not a finished product. Several teachers in the study described using AI exactly that way, to generate a first draft or brainstorm ideas, then refining everything themselves.
That pattern echoes what Cheng et al. (2025) found in their study on AI and writing performance. Students who brought their own questions and purposes to the AI interaction got significantly better results. Agency shaped the outcome. The same logic applies to teachers. Those who approach AI with clear instructional goals and a willingness to revise get more out of it than those who expect a polished product from a single prompt. That was true in 2023, and I suspect it’s even more true now that the tools are more capable and the temptation to accept AI output at face value is stronger.
Districts Were Leaning Toward Support, Not Restriction
One of the more encouraging findings in the report was the orientation of district leaders. “These interviewees were more focused on how to use AI well rather than on how to restrict or block its use” (p. 11). Given how many districts had moved toward outright bans in early 2023, that shift was significant by fall of that year. The panic was already subsiding for many, replaced by a growing interest in training, guidance, and responsible experimentation.
By the end of the 2023-2024 school year, 60 percent of districts either had provided or planned to provide teacher training on AI. That sounded promising at the time. But the details mattered enormously then and still do now. What kind of training? How deep? A one-hour webinar on ChatGPT prompts is very different from sustained professional development that builds pedagogical judgment around AI use.
I wrote about this problem when covering Bilbao-Eraña and Arroyo-Sagasta’s (2025) study on AI literacy for pre-service teachers. Their 8-hour intervention improved awareness and attitudes but didn’t build trust. Trust requires deeper engagement with ethics, reliability, and governance. Choi, Jang, and Kim (2023) found something similar: ease of use was the strongest predictor of whether teachers would adopt AI tools, and trust needed deliberate, sustained attention. If district training stays at the surface level, and there’s no reason to think that problem has disappeared since 2023, adoption numbers may grow without the pedagogical depth to support them.
The Equity Gap in the Data
Here’s the part of the report that should worry us most, and the part I suspect has changed the least. The data showed that historically advantaged districts were far more likely to offer AI training:
“Assuming districts’ current plans come to pass, by the end of the 2023-2024 school year, 65 percent of majority-White districts will have provided training compared with only 39 percent of districts serving mostly students of color” (p. 11).
A 26-percentage-point gap in training access. And it mapped onto another troubling pattern. Teachers in high-poverty schools were more likely to use AI to generate lesson plans. On the surface, that sounds practical. These teachers are often stretched thin, and AI can help with workload. But if the materials AI produces aren’t high quality, and if the teachers using them haven’t received training to evaluate and refine AI output, we end up with a situation where the most vulnerable students receive the least vetted instructional content.
Perkins and Roe (2025) raised a parallel concern in their chapter on the future of assessment. They argued that the idea of AI as a universal equalizer is “more myth than reality” and that infrastructure gaps create fundamentally different AI experiences depending on context. The RAND data from 2023 put numbers behind that argument. AI in well-resourced districts looked like supported experimentation with guardrails. AI in under-resourced districts risked becoming unguided reliance on machine-generated content. Same technology. Very different conditions around it.
Has that gap closed in two years? Maybe partially. But equity gaps in education tend to be structural, and structural problems don’t fix themselves through technology adoption alone. A 2026 version of this survey would tell us whether the training gap has narrowed or widened. I genuinely hope RAND is working on it.
The authors were clear about what needed to happen: “Further research on the quality of AI-generated classroom content is an essential next step in verifying its use in classroom settings” (p. 14). That call was urgent in 2024. With AI tools now significantly more powerful and more widely adopted, it’s even more pressing today.
What Schools and Districts Should Do
The RAND report captures an early stage of AI adoption in American schools. The percentages will inevitably change as access expands, but the questions raised by the report remain important. Teachers need time, guidance, and opportunities to examine AI-generated materials before using them with students.
One-off workshops are unlikely to provide enough preparation. Teachers need sustained professional learning that addresses instructional design, accuracy, bias, privacy, and the limitations of AI-generated content. They also need opportunities to test tools, discuss their experiences with colleagues, and revise their practices as the technology changes.
Schools and districts can begin by asking:
- Which AI tools are teachers currently using, including tools adopted independently?
- What student or instructional information is being entered into these systems?
- How are teachers checking the accuracy and suitability of AI-generated materials?
- Does professional learning connect AI use to subject knowledge and pedagogy?
- Are teachers in every school receiving comparable access to training and support?
- How will the district evaluate whether AI use improves student learning?
The equity finding deserves special attention. The survey projected that 65 percent of majority-White districts would provide AI training by the end of the 2023–2024 school year, compared with 39 percent of districts serving mostly students of color. Schools with fewer resources may also have less time and support for evaluating AI-generated materials. Expanding access to AI without addressing these differences could reproduce existing inequalities.
For me, this is the most important lesson from the report. Teachers were already experimenting with AI, and many were using it to create materials and reduce planning time. The quality of that use depended on the conditions around them. Training, time, local guidance, and professional judgment shaped what teachers could do with the technology. Those supports should be treated as part of AI implementation from the beginning.
Reference
- Bilbao-Eraña, A., & Arroyo-Sagasta, A. (2025). Fostering AI literacy in pre-service teachers: Impact of a training intervention on awareness, attitude and trust in AI. Frontiers in Education, 10, 1668078.https://doi.org/10.3389/feduc.2025.1668078
- Cheng, Y., Fan, Y., Li, X., Chen, G., Gašević, D., & Swiecki, Z. (2025). Asking generative artificial intelligence the right questions improves writing performance. Computers and Education: Artificial Intelligence, 8, 100374. https://doi.org/10.1016/j.caeai.2025.100374
- Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145
- Diliberti, M. K., Schwartz, H. L., Doan, S., Shapiro, A., Rainey, L. R., & Lake, R. J. (2024). Using artificial intelligence tools in K-12 classrooms. RAND Corporation. https://www.rand.org/t/RRA956-21
- Perkins, M., & Roe, J. (2025). The end of assessment as we know it: GenAI, inequality and the future of knowing. In AI and the future of education: Disruptions, dilemmas and directions (pp. 76–80). https://durham-repository.worktribe.com/output/4472558/the-end-of-assessment-as-we-know-it-genai-inequality-and-the-future-of-knowing
