As of the 2024-25 school year, fewer than one-quarter of Florida school districts explicitly addressed AI in their codes of conduct, with urban and larger districts serving diverse populations more likely to establish policies, while rural and predominantly White districts largely lacked clear guidance on student AI use.
Objective
The primary goal of this research report was to examine how Florida public school district codes of conduct address student use of artificial intelligence, particularly in relation to academic dishonesty and technology misuse. The researchers aimed to understand the extent to which AI is explicitly included in discipline policies, identify patterns in policy adoption across different types of districts, and provide guidance for districts developing AI-related conduct codes. This analysis represents one of the first systematic examinations of how K-12 school districts are adapting their disciplinary frameworks to address the rapid emergence of AI tools in educational settings.
Methods
The research team conducted a comprehensive analysis of 73 codes of conduct from all 67 county-based Florida public school districts plus six specialty districts during the 2024-25 school year. Codes of conduct were systematically searched for key terms related to artificial intelligence, academic dishonesty, and technology misuse using NVivo qualitative software. The researchers employed binary coding to capture whether districts mentioned AI explicitly or generally, included academic dishonesty policies, and included technology misuse policies. They further categorized districts based on their approach to AI regulation and disciplinary guidelines. To examine variation across district characteristics, researchers obtained demographic and enrollment data from the Florida Department of Education, including information on race/ethnicity composition, English Learner status, exceptional student status, and free/reduced-price lunch eligibility. Districts were grouped into quartiles based on these demographic characteristics and compared using conditional means to identify differences in AI policy adoption.
Key Findings
The analysis revealed significant gaps in AI policy development across Florida school districts. Only 23.3% of districts explicitly referenced AI in their codes of conduct, with all references using the term "artificial intelligence" or "AI" directly. Among districts that did address AI, 94% included it within academic dishonesty sections, while only 29% referenced AI in technology or digital misuse sections. Notably, no district implemented complete prohibitions of AI use; all districts with AI policies allowed conditional use under certain circumstances, though the specificity of these conditions varied considerably.
Demographic patterns revealed substantial disparities in policy adoption. Urban districts were significantly more likely to reference AI (41%) compared to rural districts (0%). Larger districts demonstrated higher adoption rates, with 28-56% of districts in the top enrollment quartiles including AI policies versus only 5-6% in the smallest districts. Districts serving higher proportions of racial minority students and English Learners were more likely to include AI references, while districts with the highest concentrations of White students (63-87% White) made no explicit AI references. Similarly, no district in the highest free/reduced-price lunch quartile incorporated AI policies, suggesting that the most economically disadvantaged communities lacked explicit guidance on AI use.
Implications
This research contributes significantly to understanding how K-12 education systems are responding to the integration of AI technologies. The findings highlight the uneven development of AI discipline policies across Florida, with potential implications for educational equity. The absence of clear AI guidelines in over three-quarters of districts leaves educators and students without explicit direction on appropriate use and potential consequences for misuse. The demographic disparities suggest that smaller, rural, and predominantly White districts may lack the administrative capacity or resources to develop comprehensive AI policies. The predominant linkage of AI policies to academic dishonesty rather than broader technology misuse indicates that current frameworks may not adequately address the full range of potential AI applications and misuses, including deepfakes, misinformation, and cyberbullying. The report provides practical examples of comprehensive AI policy language that can serve as models for districts developing or revising their codes of conduct.
Limitations
The study acknowledges several important limitations. The analysis focused exclusively on codes of conduct and student handbooks, potentially missing other sources of AI guidance that districts may provide through separate policies, teacher training materials, or curriculum documents. The research represents a snapshot of the 2024-25 school year, capturing policies during a rapidly evolving period when many districts were still developing their approaches to AI. The descriptive nature of the analysis does not establish causal relationships between district characteristics and policy adoption, though it effectively identifies patterns and disparities. Additionally, the study did not examine actual implementation or enforcement of these policies, nor did it assess student or educator awareness of existing provisions.
Future Directions
The researchers recommend several priorities for future research and policy development. School districts should develop explicit AI references in codes of conduct, clearly delineating permitted and prohibited uses along with potential consequences for misuse. State and regional education consortiums should provide support to smaller rural districts that may lack resources for independent policy development, addressing the capacity gaps identified in the study. Future policies should expand beyond academic dishonesty to address AI's potential role in creating deepfakes, facilitating bullying, spreading misinformation, and other forms of technological misuse. Researchers should continue monitoring how codes of conduct evolve as AI technology advances and state guidance emerges, examining the relationship between policy adoption and actual disciplinary outcomes. Additional research should explore student awareness and understanding of AI policies, educator training needs, and the effectiveness of different policy frameworks in promoting responsible AI use while maintaining educational benefits. Longitudinal studies examining the impact of AI discipline policies on student outcomes would provide valuable insights into best practices for integrating AI into educational environments.
Title and Authors
Title: "Disciplining AI Use: How School District Codes of Conduct Govern Students' Use of AI in Florida"
Authors: F. Chris Curran and Jiyeon Goo, University of Florida
Published On: October 2025
Published By: Education Policy Research Center, University of Florida (Research Report)