How colleges and universities are adopting AI in higher education: a global comparative analysis

Synthesis: Chan (2026) built the AI Institutionalization Index (AIII) — 20 binary indicators across five domains (governance & leadership, academic structure, research ecosystem, policy & compliance, campus life) — and coded the official websites of all 100 universities in the Times Higher Education 2026 World University Rankings (reviewed January–May 2026, English-language pages only, conservative yes-only coding). The headline finding is a discourse–structure disjuncture: every one of the 100 has an AI task force, 100% offer a master's AI degree, and 61% offer an AI certificate — but only 12% have executive AI leadership, only 5% require an introductory AI course for undergraduates, and 0% have an AI accessibility policy despite 93% deploying AI-powered campus services. A second headline: AI institutionalization is not associated with prestige — the correlation with global rank is near zero (r ≈ 0.04; with THE score r ≈ −0.05) — so adoption is driven by institutional strategy and regional context, not rankings. Regional differences are significant (ANOVA F = 5.86, p < .001): North America highest (M = 0.63), then Oceania/Canada (0.58), Europe (0.51), Asia (0.50). The result is a six-model typology in which only a small subset — mostly large US publics like Purdue, UT Austin, Illinois, Georgia Tech, Carnegie Mellon — reach "deep institutionalization" (AIII > 0.80). The evidence boundary is substantial and the author states it: the AIII is a new instrument with no established validity or reliability testing; the coding is binary (no intensity), website-only (may undercount, especially in the Global South), controls for nothing (endowment, enrollment, public/private), and the sample is elite top-100 only.

Summary

AI and generative AI have become central strategic priorities for universities worldwide — new AI degree programs, research centers, and policy statements are proliferating — "yet the extent to which institutions formally embed AI into their governance, academic, and policy structures remains unclear." Prior work catalogued adoption policies at top universities (Parker et al. 2025) or developed a single AI-attitude scale (Li & Wang 2025), but nothing systematically measured how deeply AI is embedded across institutional domains. This study constructs the AIII to fill that gap: 20 indicators, each coded 1/0 from explicit website evidence (strategic plans, catalogs, policy documents, announcements), organized into five sections — governance & leadership (task force, community of practice, VP/Chief AI Officer), academic structure & programs (AI school/department, bachelor's and master's degrees, certificates/minors, required intro courses, universal AI literacy requirement), research ecosystem (interdisciplinary AI center), policy & compliance (faculty/student/university-wide policies, staff upskilling, web-posted policy, AI accessibility policy), and campus life (AI-powered services, student groups, professional development). The unweighted index sums to a 0–1 range; the weighted version assigns depth-3 to deep institutionalization (literacy requirements, required coursework, leadership roles), depth-2 to structural integration (degrees, centers, university-wide policies), and depth-1 to symbolic/supportive elements (student groups, events, public-facing policies), grounded in institutional theory's distinction between substantive structural change and symbolic or loosely coupled practices (Powell & DiMaggio 2012).

The mixed-methods design adds a qualitative layer: the same websites were read for discursive framing (innovation, risk, opportunity, disruption, responsibility, inevitability), letting the analysis compare "how institutional discourse aligns with or diverges from observed structural practices." The two research questions ask how far AI is institutionalized across governance, curriculum, policy, and support, and how universities discursively construct AI worldwide — with hypotheses H1 (significant regional differences) and H2 (public discourse reflects stronger innovation/transformation commitments than formal structures evidence). The method's transparency choices are deliberate and double-edged: only English-language pages (all 100 universities maintain one, enabling standardization), conservative coding (present only when explicitly documented, reducing false positives), and official webpages only (capturing institutional signaling rather than informal practice). The analysis is comparative and descriptive across the elite top 100; the author explicitly does not claim to measure internal practice — "I cannot know the degree of institutionalization from what is available on their websites."

Key Findings

• The structural indicators move in opposite directions from the symbolic ones. Across all 100 universities: AI task forces and communities of practice are universal (100%), master's AI degree programs are universal (100%), and 61% offer AI certificates — but executive AI leadership exists at only 12%, undergraduate AI course requirements are rare (5%, with fewer than 5% requiring intro AI at both levels), and AI literacy requirements for all undergraduates are similarly scarce. The deep-institutionalization layer (required coursework, executive leadership, universal literacy) is where the field is thin; the visible layer (task forces, programs, public policies) is saturated.
• The accessibility gap is the starkest single number: 0% AI accessibility policy against 93% AI campus services. No sampled university publishes a standalone AI accessibility policy, even as 93% deploy AI-powered campus services (chatbots for registration, housing, IT). The author offers a charitable interpretation — accessibility obligations may be embedded in general disability-services policies rather than standalone AI documents — but the finding stands as measured: accessibility requirements for AI tools are not visible anywhere in the institutional public record.
• AI institutionalization is independent of prestige. AIII scores correlate near zero with global rank (r ≈ 0.04) and negligibly with THE institutional score (r ≈ −0.05; note rank and score themselves correlate −0.97***). The universities leading AI institutionalization are not the top-ranked ones: Purdue (THE rank 85) tops the AIII ranking (0.97), while Oxford (rank 1) sits at 0.45 and Tsinghua (rank 12) at 0.36. Adoption "reflects institutional strategy, national policy contexts, and organizational priorities" rather than hierarchical positioning.
• Regional stratification is significant but the regions are close. ANOVA across regions: F = 5.86, p < .001 — North America M = 0.63 (SD = 0.16, n = 37), Oceania 0.58 (n = 6), Canada 0.58 (n = 2), Europe 0.51 (SD = 0.08, n = 35), Asia 0.50 (SD = 0.12, n = 20). North America has both the highest median and the widest range; Asia the greatest dispersion. Post hoc comparisons confirm North America significantly outperforms Europe and Asia — a real but modest regional gradient on top of a globally middle-heavy distribution.
• The distribution is right-skewed around a moderate middle. Most universities cluster at AIII ≈ 0.45–0.65; few exceed 0.80; a small elite group sits near 0.95. The typology this yields has six models: Deep Institutionalization Leaders (comprehensive integration — Carnegie Mellon, Georgia Tech, Illinois, Texas Austin, Purdue), Comprehensive AI Ecosystems (degrees, centers, policies but no universal curriculum mandates — MIT, Stanford, Berkeley, NUS), Research + Policy Hybrids (the dominant global model — Toronto, Michigan, UCL, UCLA, UBC, Groningen, UNSW), Research-Dominant Academic Models (AI schools and degrees, limited governance discourse — Tsinghua, Peking, Fudan, Zhejiang, Shanghai Jiao Tong), European Governance–Research Models (strong frameworks and research, limited curricular change — ETH Zurich, EPFL, TUM, Heidelberg, Humboldt, Bonn), and State-Aligned AI Literacy Models (NTU, KAIST).
• Weighting sharpens the typology but does not change its structure. The 3/2/1 weighting scheme modestly shifts absolute scores and can move mid-range institutions between adjacent categories (e.g., hybrid → comprehensive ecosystem), but institutions with comprehensive integration remain leaders regardless — "the classification is robust to reasonable variations in weighting."

What this means for practice

• A task force is not institutionalization. The 100%-task-force / 12%-executive-leadership split is the study's practical warning for any institution auditing its own AI posture: the visible structures (committees, programs, policy webpages) are cheap and universal; the binding commitments (an accountable executive role, required coursework, universal literacy expectations) are rare even among the global elite. Self-assessment against the AIII's 20 indicators exposes which layer an institution has actually built.
• Curriculum is where elite universities are furthest behind their own rhetoric. Master's programs are universal but undergraduate AI requirements sit at 5% — the graduate-facing, revenue-visible layer is built while the universal-literacy layer is not. For institutions that have publicly committed to AI literacy for all students, the measured norm is that almost no peer has operationalized it, which cuts both ways: little template to copy, and little competitive pressure excusing the gap.
• Accessibility policy for AI is an unoccupied field. With 0% of 100 elite universities publishing AI accessibility policy while 93% run AI campus services, any institution that writes one — even a short statement that AI tools in teaching and administration must be accessible to disabled students — is ahead of the entire measured field, and the finding is a concrete accessibility gap for the Accessibility and equity agendas.
• Prestige is no excuse in either direction. The near-zero rank correlation means a highly ranked university cannot claim AI leadership by reputation, and a lower-ranked one cannot claim the field is closed. Institutional strategy and regional policy context — not ranking — drive adoption, which puts the initiative within any institution's reach regardless of position.
• Read the numbers with their measurement caveats. The AIII is a first-generation, unvalidated instrument; binary coding cannot distinguish a one-person task force from a functioning governance structure; website-only evidence undercounts institutions that document elsewhere (the author flags the Global South especially); and the elite top-100 sample says nothing about teaching-focused or regional universities. The study is best used as a comparative map and audit template, not as a scorecard.

Limitations

• The index is unvalidated. The author states the AIII "has neither been tested nor evaluated by teacher-scholars or practitioners in terms of its validity and reliability" — no convergent/discriminant validation against other measures of institutional AI capacity exists.
• Binary coding loses intensity and scale. A variable is 1 only when explicitly documented; there is no measure of program size, funding, uptake, or whether a policy is enforced.
• Website-only evidence may undercount. Institutions may not update English websites (Global South especially), and requirements may live in faculty-senate records or committee minutes invisible to the coder — the author notes the 5% undergraduate requirement figure "may [have] curricular requirements buried in faculty senate records."
• No controls. Endowment, enrollment, public/private status, and national regulatory environment are uncontrolled, so regional and prestige analyses are descriptive associations.
• Elite top-100 sample only. "Specialist" institutions (e.g., life-sciences-focused) may distort integration levels, and non-elite, teaching-focused, and regional comprehensive universities — the majority of the sector — are unrepresented.

Citation

Chan, R. Y. (2026). How colleges and universities are adopting AI in higher education: a global comparative analysis (EdWorkingPaper No. 26-1576). Annenberg Institute at Brown University.