AI in Educational Leadership: Navigating Challenges and Opportunities

The Research Behind This Article

I had the privilege of contributing to a scoping review that systematically examines how artificial intelligence is reshaping educational leadership. The paper, "Navigating the challenges and opportunities of artificial intelligence in educational leadership: A scoping review", authored by Ana-Ines Renta-Davids, Marta Camarero-Figuerola, and Mar Camacho, was published in the Review of Education, a peer-reviewed journal of the British Educational Research Association (BERA) hosted by Wiley.

The full paper is available at: Wiley Online Library (DOI: 10.1002/rev3.70101)

This article summarizes the key findings and reflects on their implications for anyone working at the intersection of AI and education -- a space I have inhabited for years through Saturdays.AI and my advisory work in public policy.

Why This Research Matters Now

The integration of AI into educational settings is no longer a future scenario. Schools and universities worldwide are confronting the reality of AI-powered tools in classrooms, administrative offices, and policy discussions. Yet while much attention has been paid to AI's impact on students and teachers, far less has been devoted to understanding how AI transforms the role of educational leaders -- the principals, directors, and administrators who must make strategic decisions about AI adoption, governance, and implementation.

This gap is significant. Educational leaders are the ones who decide whether and how AI tools are deployed, who set policies around data privacy and algorithmic transparency, and who must navigate the ethical complexities that arise when automated systems influence decisions about students and staff. Without understanding the specific challenges and opportunities that AI presents to these leaders, we risk deploying powerful technologies without adequate human oversight.

AI compels educational leaders to engage in lifelong learning and to rethink traditional management frameworks. As AI reshapes decision-making processes, leaders must balance incorporating AI with the essential human elements that underpin effective leadership.

-- Renta-Davids, Camarero-Figuerola & Camacho (2025), Review of Education

What We Did: A Systematic Scoping Review

The study followed a rigorous scoping review methodology to synthesize the existing literature on AI's impact on educational leadership. The research team identified and analyzed the current body of published work, mapping the landscape of what is known, what is emerging, and where the gaps lie.

The review identified two main categories and 10 distinct themes that organize the current understanding of AI in educational leadership:

Key Finding 1: AI Enhances Decision-Making -- But With Caveats

The review found that AI has genuine potential to improve educational decision-making through data-driven insights and automation of administrative tasks. School leaders can leverage AI for tasks like student performance analysis, resource allocation optimization, scheduling, and early warning systems for at-risk students.

However, the research is clear that this potential comes with significant conditions. Implementation requires careful consideration of ethical, equity, and human-centred concerns. AI systems trained on historical data can perpetuate existing biases. Predictive models can label students in ways that become self-fulfilling prophecies. Automated administrative decisions can lack the contextual nuance that experienced educators bring.

Key Finding 2: Leaders Need New Competencies

Perhaps the most actionable finding is that educational leaders must develop digital literacy and AI competence to critically assess AI outputs and mitigate risks. This goes beyond basic digital skills. Leaders need to understand enough about how AI systems work to ask the right questions: What data was this model trained on? How does it handle edge cases? What are the failure modes? Who is accountable when the system makes a mistake?

The specific competency areas identified include:

• Algorithmic literacy: Understanding how AI systems make recommendations and where bias can enter
• Data privacy governance: Navigating the complex landscape of student data protection, consent, and regulatory compliance
• Ethical reasoning: Making judgement calls about when AI-assisted decisions are appropriate and when human oversight must prevail
• Change management: Leading staff through the organizational transformation that AI adoption requires
• Critical evaluation: Assessing vendor claims, understanding system limitations, and avoiding hype-driven adoption

Key Finding 3: The Professional Development Imperative

The review emphasizes the necessity of continuous professional development for both leaders and staff to ensure effective and ethical AI integration. This is not a one-time training exercise. As AI capabilities evolve rapidly, the knowledge required to govern these systems must evolve alongside them.

This finding resonates deeply with my experience at Saturdays.AI, where we have trained 30,000+ alumni across 12 countries. The demand for AI literacy is not confined to technical roles -- it extends to every level of organizational leadership. The leaders who will navigate AI successfully are those who commit to ongoing learning, not those who delegate all AI decisions to a technical team.

The Geographic Landscape: A Telling Imbalance

One of the most revealing findings concerns the geographic distribution of research. Eight studies were conducted in the USA and seven in China, with smaller contributions from Turkey, Australia, Indonesia, Northern Cyprus, Pakistan, Palestine, Qatar, Spain, and the United Kingdom.

European contributions are notably scarce and fragmented, consisting mostly of concept papers and editorials rather than empirical studies. The researchers suggest this may reflect early-stage exploration of the topic in Europe, possibly linked to differing AI governance approaches at national and regional levels.

This imbalance matters. AI governance in the EU, with the EU AI Act and its risk-based framework, creates a distinctly different environment for AI adoption in schools compared to the US or China. Research that ignores this context risks producing recommendations that do not transfer across regulatory environments.

The Leadership Readiness Gap

Perhaps the most sobering conclusion of the review is the identification of a leadership readiness gap. The research reveals not only the multifaceted challenges school leaders face with AI integration, but also the limited theoretical tools currently available to support them. There is a disconnect between the pace of technological advancement and the pace at which leadership frameworks, training programs, and governance structures are evolving to meet it.

This is not unique to education. In my work advising on AI strategy at the national level, I see a similar pattern: the technology moves faster than the institutions designed to govern it. But in education, the stakes are particularly high because the subjects of these AI-mediated decisions are children and young people.

The review reveals not only the multifaceted challenges school leaders face with AI integration, but also the limited theoretical tools currently available to support them -- highlighting a disconnect between technological advancement and leadership readiness.

-- Renta-Davids, Camarero-Figuerola & Camacho (2025)

Implications for Practice

Based on the research findings, several practical implications emerge for educational leaders, policymakers, and AI practitioners:

Looking Forward

The integration of AI into educational leadership offers genuine opportunities to improve efficiency, personalize learning, and enhance decision-making. But realizing these benefits requires a holistic approach that prioritizes equity, transparency, and professional development. Educational leaders can leverage AI to drive positive change -- but only if they maintain the human-centred values that make education meaningful.

The research agenda ahead is clear: we need more empirical studies, particularly from European and Global South contexts. We need longitudinal research that tracks the long-term impacts of AI on school leadership practices and student outcomes. And we need to develop the theoretical frameworks and practical toolkits that will help educational leaders navigate an increasingly AI-mediated future with confidence and integrity.

For me personally, this research connects the threads of work I have pursued across different domains -- from building AI education programs at Saturdays.AI, to advising on national AI strategy, to the technical work of building AI systems. The common thread is the same: AI is only as good as the human judgement, governance, and values that guide its deployment.