Anticipatory governance and leadership for AI implementation in higher education: A scoping review

Synthesis: This scoping review of 19 sources (2020–2025) examines how anticipatory governance and leadership are conceptualized and operationalized for AI implementation in higher education. It finds that institutions must shift from reactive to proactive, foresight-driven leadership emphasizing stakeholder engagement, data literacy, collaboration, and inclusive policy frameworks. Empowering and distributive leadership styles increase AI adoption, yet formal senior AI roles remain rare. A persistent theory-implementation gap is driven by weak policy frameworks and limited digital infrastructure, especially in the Global South. The review identifies a lack of non-Western research and an absence of longitudinal or causal evidence as key gaps.

Core Finding

Effective AI governance in higher education depends on adopting anticipatory models — proactive, participatory, adaptive decision-making using foresight tools and stakeholder co-creation — rather than reactive crisis management. Realizing transformative change requires going beyond managerial rationales to participatory and inclusive leadership that is data-literate and forward-thinking.

Leadership and Governance Themes

The dominant theme is a shift from reactive to proactive leadership. Empowering and distributive leadership styles were associated with higher faculty engagement, willingness to innovate, and openness to change. Senior AI roles (e.g., Chief AI Officer, AI policy officers) are emerging enablers but remain rare: only 7% of institutions created senior AI leadership roles despite 49% viewing AI as a strategic priority. AI leadership is becoming increasingly distributed across executives, faculty, managers, instructional designers, and AI specialists rather than centralized in senior administration.

Institutional Readiness and Upskilling

Many institutions remain at an early stage of preparedness. Aligning vision, mission, and curricula with the Fourth Industrial Revolution, investing in AI literacy and hands-on training for faculty and staff, and addressing technophobia are emphasized. Change Management models (Valente's contagion model; Rieber & Welliver's five-stage framework) and cross-functional AI task forces support system-level change.

Equity and the Global South

A notable theory-implementation gap is driven by weak policy frameworks and limited digital infrastructure in the Global South, including the Arab world, Sub-Saharan Africa, and Southeast Asia. Western-centric governance models often do not fit these realities. The review notes limited non-Western research, dominance of conceptual or self-reported evidence, and an absence of longitudinal or causal studies as key gaps.

Relevance to the knowledge base

This paper strengthens the knowledge base's treatment of AI Governance and AI policy in higher education. It connects institutional leadership to ethical and equitable AI adoption, and it complements empirical adoption studies (e.g., Exploring Organisational Drivers and Innovation Attributes of Artificial Intelligence Adoption in Higher Education) with a systemic, futures-oriented governance lens. Its emphasis on stakeholder engagement, faculty upskilling, and Global South equity aligns with the knowledge base's coverage of teacher roles, AI Literacy, and context-sensitive AI integration. It informs policy discourse by framing governance as an anticipatory, participatory process rather than a compliance exercise.

What this means for practice

• Administrators. Shift from reactive crisis management to anticipatory governance, building foresight and stakeholder co-creation into AI decisions before adoption becomes urgent.
• Create senior or distributed AI leadership: only 7% of institutions had established such roles even though 49% treated AI as a strategic priority, and cross-functional AI task forces support system-level change.
• Fund AI Literacy and hands-on training for faculty and staff, sequencing it with established Change Management models (Valente's contagion model; Rieber and Welliver's five-stage framework).
• Test governance templates against local infrastructure and policy capacity before adopting Western-centric models, which the review found fit Global South contexts poorly.

Limitations

• Scoping review of 19 heterogeneous sources (2020-2025) with uneven regional representation and a concentration of Western studies; the authors state the conclusions are not intended to be universally generalizable.
• Most included studies used conceptual or cross-sectional designs, so the review cannot establish longitudinal or causal effects of anticipatory governance.
• No formal trustworthiness analysis of the included studies was conducted, which the authors flag as limiting the robustness of the analysis.

Citation

Baroudi, S. (2026). Anticipatory governance and leadership for AI implementation in higher education: A scoping review. International Journal of Educational Technology in Higher Education.