AI use in Texas schools is outpacing the rules that guide it. Texas 2036’s report explores how clear guidance, student safeguards and teacher training can help schools navigate AI while keeping educators in control and student learning at the center.
Key Findings
- In a fall 2025 survey of Texas school and district leaders by the University of Texas at Austin and the Texas Association of School Administrators, 86% said teachers were using AI and 61% reported student use. Yet only 29% reported a district policy governing student AI use, and just 20% had one for teachers and staff.
- The report analyzes the mixed evidence on AI and learning. Where some studies find real gains, others find real harms, and many find both. The difference often comes down to how tools are designed, how teachers guide their use and whether students are equipped to think critically and independently.
- Forty-two states have issued some form of statewide AI guidance or model policies for schools. Texas is among the eight that have not.
- Nearly 85% of Texas voters say protecting student data and privacy is extremely or very important if schools use AI. More than three-quarters prioritize safety and quality standards for AI tools, and 71% say teaching students to use AI responsibly and ethically is important.
Four Priorities for Texas Lawmakers
Building on Texas 2036’s recently released legislative agenda, the report identifies four priorities for responsible AI use in K–12 education:
1. Establish statewide guidance.
Give every school district a common, evidence-based foundation for responsible AI use, building on laws already enacted by the Legislature.
2. Put student safety first.
Ensure AI tools meet appropriate standards for data privacy, security and educational effectiveness before they ever reach Texas classrooms.
3. Equip Texas teachers.
Ensure educators have access to training and resources that help them recognize and respond to AI’s risks, understand its limitations and make informed decisions about its use in classroom instruction.
4. Prepare students for an AI-driven future.
Build students’ critical AI literacy skills in an age-appropriate manner, emphasizing critical thinking, responsible use and independent learning.
Methodology
This report reviews research on AI in education to inform Texas policy, prioritizing evidence of cause and effect on student learning, development and educator practice.
The research is anchored by three sources:
- Stanford University’s AI Hub for Education review (2026): 20 qualifying papers examining evidence relevant to K–12 education.
- Huang et al. (2026): A meta-analysis of 36 studies on generative AI feedback and academic performance.
- Zhao et al. (2025): A meta-analysis of 29 studies on generative AI and higher-order thinking, including critical thinking, creativity and problem-solving.
Additional studies, national and Texas surveys, policy reports, state laws and practitioner frameworks inform the analysis. The literature search concluded in August 2026; survey data and state policy documents are current as of October 2026.
The evidence remains early. Much of the research comes from outside the United States, and the reviews draw more heavily on higher education than K–12 settings. Causal studies are limited, some are not yet peer reviewed, and survey findings are self-reported. Findings should be read as early signals rather than settled conclusions.
About the Author
Renzo Soto serves as Deputy Director of Education and Workforce Policy at Texas 2036. He covers K-12 and AI policy, with a focus on college and career readiness and the responsible and effective use of technology in education, as well as school finance and assessment and accountability.
