AI adoption accelerating faster than formal oversight
The stride of AI adoption has accelerated sharply. As of March 2025, 28 US states have published or adopted AI guidance for K-12 education with most of these states looking to integrate AI with specific instructional and support-related aims. Some states have also implemented AI technology for particular purposes like tracking and identification as well. For example, the Kentucky Department of Education developed an Early Warning Tool that uses AI to analyze district-level student data to help schools identify students who may be at risk of dropping out or failing classesi.
Generative AI tools are widely utilized by educators for daily tasks. A March 2024 report by the Center for Democracy & Technology found that the percentage of K-12 teachers who reported using a generative AI tool for personal or school use increased by 32 percentage points, reaching 83%, between the 2022-2023 school year and 2023-2024ii.
There were nearly 14,000 security events and over 9,000 confirmed incidents recorded in just 18 months.i However, recent trends reveal that one of the most significant vulnerabilities may, in fact, be students themselves.
For ASBO members, the most relevant AI use cases deal with efficiency, service and better operational support.
Here are some AI use cases in school operations:
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AI parent chatbots for routine queries: Integrating school-branded chatbots into parent portals significantly reduces the administrative burden on front-office staff. These systems may be able to respond to a significant proportion of routine inquiries, including fee deadlines, attendance checks and calendar events in multiple languages, depending on the solution deployed and the district’s operating environment.iii
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Collaborative AI assistance in reporting and assessments: AI-driven text generators streamline student reporting by drafting first-pass, personalized report-card comments based on attendance, assignment notes and grades. Rather than replacing the educator, these tools serve as a useful starting point; teachers review, edit and approve every comment, helping reduce the time required to prepare report card comments, depending on the size of the class, the quality of the underlying data and the level of educator review required.
Similarly, AI can assist with auto-grading routines, objective assessments — such as multiple-choice or short-answer sections — in some studies, demonstrating high levels of agreement with human markers for certain objective assessment types. While an exam for 300 students can have its objective portions graded in minutes, human oversight and final review remain essential for all high-stakes, critical assessments to ensure complete evaluation integrity3.
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Predictive early warning models: Machine learning applications evaluate student attendance, grades and behavioral patterns to identify individuals at risk of academic failure or dropping out. In some implementations, these tools may identify potential concerns several weeks before key assessment periods, allowing counselors to consider earlier interventions. As these tools carry long-term fiscal implications for district funding and retention, they require thorough human supervision to prevent algorithmic bias.
Many K-12 AI use cases focus on supporting decision-making and administrative efficiency rather than replacing human oversight.
Governance cannot be an afterthought
One challenge associated with AI adoption is that implementation can move ahead of policy frameworks. In many districts, usage is already widespread while formal governance is still catching up. This creates exposure around privacy, procurement and compliance. Among the surveyed members of the Consortium for School Networking (CoSN), 76% of respondents said that the district leaders are helping guide AI usage and offering recommendations. The majority are in the early stages: 41% have opened discussions about AI in schools, 23% are starting to form a district-level vision and just 12% have clear oversight in placeiv.
A significant operational challenge involves data readiness. Approximately 61% of districts report having siloed or disorganized data architectures, while just 1% state that their data is fully prepared and secure for AI utilization4.
Protecting privacy: FERPA, COPPA and vendor due diligence
School business officials must ensure that AI deployment complies fully with the Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA) and state-level privacy statutes. While FERPA permits the use of cloud computing and third-party vendors under the “school official exception” policy, the district is generally expected to maintain appropriate oversight and control over student data. Contractors need to be legally restricted from using personally identifiable information (PII) for any unauthorized purpose.
Effective controls and safe practices
To support district security and operational resilience, business leaders may wish to consider the following controls:
AI is no longer limited to classroom discussions. It is increasingly becoming an operational consideration for school business officials. Vendor due diligence now needs to cover access controls, cloud security, legal liability and continuity if a provider fails or exits the market. That level of oversight is essential if districts want innovation without avoidable exposure.
Gallagher’s team of education specialists understands the unique fiscal constraints, compliance mandates and operational pressures facing K-12 districts. We provide risk management assessments, vendor contract reviews and tailored cybersecurity strategies designed to help institutions strengthen their security, compliance and operational resilience.
Contact a Gallagher specialist to learn more about approaches that may help support your district’s technology governance and risk management objectives.