Exploring basic school leaders' AI readiness: The role of professional development

Synthesis: Awodiji and Adeoye (2026) interviewed eight basic school leaders — heads of department, principals and vice principals from public and private schools in Ibadan, Oyo State, Nigeria — to examine readiness for AI in education and the role of professional development. The semi-structured interviews ran 35 to 60 minutes and were analyzed thematically in ATLAS.ti. Awareness was broad but shallow: leaders treated AI as an extension of familiar digital tools, and most learned through the internet, colleagues or chance community seminars rather than institutional training. None reported formal, leadership-focused AI training, so professional development emerged as the missing link between positive attitudes and practice. Structural barriers — unreliable electricity, high data costs and scarce devices — dominated, and public schools carried the heavier burden, though attitudes and ethical worries were shared. Framed by the Technology Acceptance Model and UTAUT, the study concludes that leaders are eager but not ready, and that perceived usefulness cannot compensate for absent facilitating conditions.

Key Findings

1. Awareness outran understanding. Leaders recognized AI as progress but equated it with computers, smartphones and internet platforms rather than a distinct system with its own capabilities.
2. Formal AI professional development was effectively absent. None of the eight participants reported government-sponsored workshops or in-service training dedicated to AI; several said their schools offered none.
3. Learning was informal and self-directed. The internet, search engines, colleagues and a church seminar given by a visiting computer scientist supplied most knowledge, which participants judged inadequate.
4. Structural deficits bound readiness more than motivation. Unreliable electricity, costly data, unstable networks and limited device access constrained even leaders who called themselves ready to use AI.
5. Attitudes were positive but double-edged. Participants saw AI improving lesson planning, personalized learning and administrative records, while worrying about overreliance, plagiarism, privacy and AI displacing teachers.
6. Private schools were better resourced, but attitudes converged. Private leaders reported stronger infrastructure and digital access; public leaders faced underfunding, yet both shared ethical concerns and training needs.
7. Professional development was named the missing link. Structured, context-specific, sustained training — not generic ICT modules — would convert eager but unevenly prepared leaders into capable AI leadership.

Awareness Without Conceptual Depth

The first research question asked what these leaders know about AI, and the answer was more troubling than simple ignorance. They recognized AI and tied it to classroom support, examination preparation and instructional delivery, but collapsed it into the digital tools they already used — computers, phones, online platforms. One participant framed AI around preparing students for examinations; another equated it with "artificial, man-made intelligence" as a sign of educational progress. The authors treat this conflation as consequential: leaders who see AI as ordinary ICT will not prioritize AI-specific professional development, governance or data systems. They trace shallow awareness to the absence of organized AI literacy programs. AI literacy here means the gap between functional familiarity and informed leadership judgment.

Professional Development as the Missing Link

The third research question probed AI training, and the findings were unanimous: structured, institution-led professional development did not exist in these schools. Participants described self-learning through phones, computers and web search, peer conversations, and occasional events such as a church seminar delivered by a computer scientist. These pathways were useful entry points but inadequate for practice; one leader noted that checking online does not guide them on how to use AI in school administration. Because Nigeria's education system is centrally governed, leaders wait on ministry directives rather than building AI leadership locally. Participants recommended sustained, school-specific training embedded in teacher education rather than one-off workshops. Professional development is the study's central explanatory variable, and the national policy-practice gap leaves it unfunded.

Structural Barriers and the Sectoral Divide

The fourth and sixth research questions examined obstacles and public–private differences, and the barriers participants named were overwhelmingly material. Unreliable electricity, poor connectivity, high data costs and limited device access recurred; one leader said that without data or money, you can only mention AI, you cannot use it. Readiness is a product of socio-technical conditions, the authors conclude — even a motivated leader cannot use tools infrastructure will not support. Private leaders reported greater exposure to devices and internet resources; public leaders remained constrained by systemic underfunding. Yet attitudes, ethical anxieties and training gaps were strikingly similar across sectors. Their concerns — plagiarism, overreliance and data Privacy — sit where equitable AI leadership requires judgment rather than tool familiarity.

What this means for practice

• Administrators. Treat AI readiness as an infrastructure problem: audit power, connectivity and device access before launching AI initiatives.
• Administrators and system leaders. Build sustained, school-specific AI leadership training into professional development rather than relying on self-directed internet learning alone.
• Policymakers. Close the policy–practice gap with mandated, funded AI competency programs reaching public and private schools alike.
• Professional development providers. Design for depth rather than tool tutorials: cover AI ethics, data governance and change management, and pair training with resource provision.

Limitations

• The authors acknowledge the absence of a clearly defined theoretical framework as a possible constraint on the richness and explanatory sufficiency of the analysis.
• The findings rest on participants' self-reports, exposing them to anecdotal bias and overreporting of readiness, and cannot be generalized beyond eight leaders.
• A single data source limited methodological triangulation; the authors call for future work combining interviews, documents and observations.

Connected Concepts

• Educational Development
• AI Literacy
• AI in Education
• Technology Adoption Models
• Self-Directed Learning
• Administrators
• Educational AI Policy
• Equity

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Citation

Awodiji, O. A., & Adeoye, M. A. (2026). Exploring basic school leaders' AI readiness: The role of professional development. Computers and Education Open, 11, 100409.