Anticipatory governance for AI in the university: what the global evidence says
The object: how AI is governed in the university, not just the classroom
The chapter’s first two entries look at AI from the syllabus level —what to do with an LLM in a specific class, how to write a course policy. Baroudi shifts scale: a scoping review of 19 academic and grey-literature sources (2020–2025) on how higher education institutions are governing, or trying to govern, the transformation AI brings at the organizational and system level.
The finding: anticipatory governance, not reactive governance
Across the literature reviewed, the recurring model is “anticipatory governance”: foresight, active engagement from faculty, students, and other stakeholders, and a shift in the traditional role of leaders and educators toward profiles that are data-literate, inclusive, collaborative, and forward-looking. The underlying idea is simple but demands reorganizing the institution: don’t wait for AI’s consequences to land and then react, but anticipate scenarios and build institutional capacity before the problem reaches the classroom.
The gap that matters for this chapter
What matters most for this chapter’s throughline is the gap the review finds between theory and implementation: anticipatory governance frameworks exist in the literature, but run into weak policy frameworks and limited digital infrastructure, particularly in the Global South —the review names the Arab world, Sub-Saharan Africa, and Southeast Asia explicitly. It’s the same tension flagged when reading Mollick (individual agency without institutional capacity isn’t enough), and the same one that motivates the blockchain proposal in the previous entry (a traceability infrastructure that presupposes exactly the institutional capacity this review finds missing across much of the planet). Three entries, three scales —syllabus, technical system, system of governance— for the same problem: the gap between what a conceptual framework lets us think and what an actual institution can sustain.
All three scales share an assumption none of them stops to examine: that we know what we are talking about when we say agency, authorship or responsibility. While the discussion stays practical, the assumption holds; the moment a rule has to be written, it collapses. That is the business of the next chapter.
Suggested reading
Baroudi, S. (2026). Anticipatory governance and leadership for AI
implementation in higher education: A scoping review. International
Journal of Educational Technology in Higher Education, 23(1), Article
39. https://doi.org/10.1186/s41239-026-00616-7
