Who Should Lead AI in Education—and How to Do It Right
The successful integration of artificial intelligence (AI) into education demands leadership that bridges policy, pedagogy, and technology. This leader should have a proven background in educational policy development, instructional practice, and technology implementation. A superintendent or assistant superintendent with prior district-wide technology initiatives, or a chief academic officer with strong collaboration across departments, would be well-positioned to lead. They must understand not just the administrative mechanics but also the classroom dynamics and the nuances of policy impact at the school level.
Given the complexity of AI as a tool that intersects ethics, innovation, and instruction, a compelling case can be made for designating a centralized leadership role such as a Chief AI Education Officer or an AI Leadership Team. This role should blend:
Policy fluency traditionally found in educational administrators
Instructional insight from curriculum directors
Technical literacy expected from chief technology officers
This individual or team would guide ethical AI strategy, ensure compliance with data privacy laws, and align AI initiatives with inclusive educational goals. They should be capable of setting a vision that is grounded in pedagogy and community trust, not just technological advancement.
Selection of this leader should be guided by a structured, transparent process involving multiple stakeholders, including district staff, school administrators, teacher representatives, parent councils, and IT professionals. A rigorous vetting procedure should evaluate candidates on their experience with cross-sector collaboration, change management, and digital equity efforts. For example, districts like Baltimore City Public Schools have successfully appointed Chief Innovation Officers who possess both classroom experience and technical fluency, allowing them to drive digital transformation while remaining rooted in educational outcomes1.
Strategic Priorities for AI Implementation in Schools
AI adoption in education should begin with clear, achievable goals that center student learning and teacher support. Key initiatives must include personalized learning platforms, automated administrative tools to reduce educator workload, and predictive analytics to support student achievement interventions. Each of these tools needs to be evaluated through pilot programs before scaling. Districts should partner with university research centers to assess efficacy and ensure tools align with curricular standards and data governance policies.
Implementation should follow a structured rollout plan developed by the AI leader or committee, incorporating:
Pilot programs tested in controlled academic settings, with iterative feedback loops to assess risks and benefits
AI ethics and safety protocols that clearly define how data is gathered, stored, and used, along with public transparency reports
Community engagement initiatives, such as open forums where families can ask questions about privacy, equity, and AI decision-making
Professional development that supports instructional integration of AI through hands-on training and focus on ethical decision-making
One practical initiative is the integration of AI-driven tutoring tools to supplement classroom instruction. These tools, such as those piloted in Gwinnett County Public Schools, provide individualized support in core subjects, allowing teachers to focus on higher-order instruction2. However, implementation must not be vendor-driven but needs-driven, guided by an internal AI deployment plan that includes professional development, equity audits, and data privacy protocols from the outset.
Professional Development and Stakeholder Engagement
Teachers are central to any AI integration strategy. Without robust professional development (PD), even the most advanced technology will fail to make an impact. Districts should offer tiered PD that blends initial AI literacy, ongoing instructional coaching, and peer-led communities of practice. A practical model can be drawn from the Fresno Unified School District, which uses a "train-the-trainer" approach to scale technology PD across its schools, ensuring local ownership and contextual relevance3.
Professional development should include:
Fundamentals of AI literacy and ethical considerations
Technical training on integrating AI into lesson planning and assessment
Ongoing coaching to support classroom implementation and adaptation
Opportunities for teacher leadership and peer mentoring through communities of practice
Engagement must also extend to students and families. Workshops and open forums can help demystify AI tools and foster trust. For instance, New York City Public Schools has launched digital citizenship curricula that include AI awareness components, helping students understand algorithmic decision-making and digital ethics4. These efforts build transparency and ensure that AI is not perceived as a top-down initiative but as a shared educational tool.
Ensuring Student Safety and Data Privacy
Safeguarding student information is non-negotiable. AI tools often require access to sensitive data including academic records, behavioral logs, and even biometric information. Therefore, districts must develop robust data governance frameworks aligned with federal regulations such as FERPA and state-specific privacy laws. These frameworks should be operationalized through data-sharing agreements, consent protocols, and regular audits.
To support this, districts should:
Appoint a dedicated data protection officer or create a data privacy task force
Conduct regular audits of AI tools and vendors for compliance and ethical use
Provide transparent communication with families about how data is used and protected
Implement a vendor review process that includes mandatory privacy assessments
For example, the Los Angeles Unified School District has implemented a stringent vetting process for new educational technologies, requiring vendors to complete a data privacy assessment before procurement5. This approach minimizes risks and reinforces the district’s commitment to student safety.
Building Student Competence and Confidence with AI
Beyond safety, AI education must empower students to become critical, informed users and creators of technology. Curriculum adjustments are necessary to embed AI literacy across subject areas. This includes teaching students how algorithms work, how data is collected and used, and how to question outcomes generated by AI systems. Programs like the AI + Ethics curriculum developed by MIT and piloted in select high schools offer a scalable model for this approach6.
To balance safety with empowerment, schools should:
Develop curricula that emphasize critical thinking, digital ethics, and algorithmic awareness
Include lessons on AI bias, transparency, and the societal impact of automation
Offer project-based learning opportunities using AI tools in real-world scenarios
Establish partnerships with local universities and tech companies for mentorships, bootcamps, and internships
These experiences prepare students to navigate a workforce increasingly shaped by AI while reinforcing the role of education as a launchpad for equitable opportunity.
Continuous Evaluation and Community Accountability
AI in education is not a one-time deployment but a continuous adaptation process. Leaders must establish feedback loops that include student voice, teacher input, and community advisory panels. Regular reporting on implementation progress, challenges, and outcomes should be part of board meetings and publicly accessible dashboards. These mechanisms foster accountability and allow quick pivots when needed.
Evaluation should also be longitudinal. Districts should partner with academic institutions to track the long-term impact of AI on student outcomes, educator workload, and equity indicators. For example, Chicago Public Schools' collaboration with the University of Chicago Consortium on School Research has yielded valuable insights into the effects of new initiatives over time7. Similar models can be applied to AI efforts, ensuring that decisions remain data-informed and community-centered.
Urgent Next Steps: A Call for Integrated Leadership and Sustainable Planning
AI in education requires leadership that is multifaceted, responsive, and deeply connected to the day-to-day realities of schools. This individual or team must have the credibility to lead across departments, the technical fluency to make informed decisions, and the public trust to implement programs that impact students’ lives. Municipal governments and school boards must invest in this leadership capacity as a foundational step, not an afterthought.
Long-term success will come from deliberate planning, inclusive engagement, and a relentless focus on educational equity. With the right leadership and infrastructure, school systems can not only protect students but also prepare them to thrive in an AI-driven future. As one Harvard framework puts it, “AI won’t replace educators, but educators with AI will replace those without it.”
References
Baltimore City Public Schools. “Office of Achievement and Accountability.” Accessed April 10, 2024. https://www.baltimorecityschools.org/oaa.
Gwinnett County Public Schools. “AI in Education Pilot Program Overview.” District Briefing, January 2023.
Fresno Unified School District. “Instructional Technology: Professional Learning.” Accessed April 10, 2024. https://www.fresnounified.org/dept/instructional-technology/professional-learning/.
New York City Department of Education. “Digital Citizenship and Safety Curriculum.” Accessed April 9, 2024. https://www.schools.nyc.gov/learning/digital-citizenship.
Los Angeles Unified School District. “Data Privacy Guidelines and Vendor Review Protocols.” Office of Data and Accountability, 2023.
MIT Media Lab. “AI + Ethics Curriculum.” Accessed April 9, 2024. https://aieducation.media.mit.edu/.
University of Chicago Consortium on School Research. “Partnerships with Chicago Public Schools.” Accessed April 10, 2024. https://consortium.uchicago.edu/partnerships/cps.