Universities should establish a small AI governance and evaluation foundation while beginning foundational AI literacy—not wait for one to be complete before starting the other. The governance layer gives the institution a way to identify uses, review risks, run pilots, and learn from results. Literacy helps students and staff use and assess AI critically and responsibly. Pilot evidence can then guide how the curriculum and institutional rules develop. This is a practical sequencing recommendation, not a proven universal rule: the sources support connected governance, evaluation, and competency development, but do not establish that evaluation must always come first.
What the two approaches mean
The comparison is not between two competing products or programs. It is between two kinds of institutional work that answer different questions and work best when connected.
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An AI evaluation and governance layer
Here, “evaluation layer” is an operational label for recurring institutional processes—not the name of a standardized framework. It means having a way to identify where AI is being used, decide which uses need review, gather evidence, conduct pilots, monitor effects and risks, and adjust policy or implementation. It connects governance with questions such as data protection, academic integrity, equity, reliability, and whether students are developing the knowledge and skills they need.
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The OECD’s 2026 policy paper describes evidence collection and pilot evaluations among system-level responses to generative AI in higher education. UNESCO IESALC also recommends governance structures and monitoring and evaluation mechanisms. Together, these recommendations point to more than a one-time approval committee: institutions need an ongoing way to learn about AI use and respond to what they find.
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An AI literacy curriculum
An AI literacy curriculum builds the knowledge, skills, and attitudes people need to understand and use AI critically and responsibly. UNESCO IESALC’s 2025 working-paper summary proposes knowledge of AI concepts and ethical considerations; skills to use, evaluate, and critically interact with AI tools; and attitudes and values oriented toward fairness, accountability, transparency, and equity.
The Council of Europe’s 2026 recommendation frames literacy as sociotechnical: it includes human, technological, and practical dimensions. Its institutional announcement puts the distinction plainly: “AI literacy, on this view, means understanding AI’s effects on society, not just knowing how the tools work.” Literacy therefore involves judgment about consequences and context, not simply learning how to operate a particular tool.
Why a university needs both
AI can affect teaching, learning, student support, assessment, skills, research, and institutional administration. The OECD’s 2026 policy paper identifies concerns that cut across those activities: data protection, academic integrity, equity, the reliability of generated content, and students’ knowledge and skills. A curriculum can help people make better decisions, but it cannot by itself determine which institutional uses are appropriate, monitor effects, or establish a process for responding to problems. Governance can set expectations and review uses, but it cannot substitute for the judgment people need in classrooms, research, and everyday work.
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A 2026 UNESCO IESALC regional study illustrates why institutions may need to build capacity on both fronts. It draws on 200 institutions across 19 countries in Latin America and the Caribbean. In that sample, 87% reported AI use in at least one area, 74% reported use in teaching and learning, and 26% had a formal AI strategy. The study also reports that clear policies, governance structures, and monitoring and evaluation frameworks are even less common. These figures describe that regional sample; they are not global estimates and should not be generalized to universities elsewhere.
UNESCO IESALC’s recommendations in that study connect the two kinds of work directly: develop comprehensive AI strategies with governance from the outset; establish dedicated governance structures that include students; treat AI literacy as a graduate competency; widen training; monitor and evaluate implementation; and redesign assessment with clear guidance on AI use. A curriculum supplies capabilities; evaluation and governance help the institution understand where those capabilities, policies, and assessments need to change.
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Which should universities start first?
Start with a minimum viable governance and evaluation foundation, and begin foundational literacy at the same time. “First” should mean setting enough institutional structure to make early AI use visible and reviewable—not postponing teaching until a comprehensive governance system is finished. Likewise, early literacy should not wait for a final institutional strategy.
This sequence is a reasoned synthesis of the official recommendations, not a causal finding from a comparison of universities that chose different orders. The available sources recommend governance, evaluation, competency development, and curriculum work together; they do not prove that one must always precede the other. An institution with an urgent capability gap may need to make literacy training an immediate priority, while still assigning responsibility for risks and review. An institution already seeing AI use without clear oversight may need to establish its review process quickly while training begins.
| Decision question | What an evaluation layer contributes | What a literacy curriculum contributes |
|---|---|---|
| Can the institution identify and respond to risks? | A process to identify uses, review relevant risks, and monitor effects. | People better prepared to recognize limitations and make responsible choices. |
| Can the university tell whether a use is helping? | Pilot evidence and recurring review of results and unintended effects. | Knowledge and skills to use, assess, and critically interact with AI. |
| How should the institution update its approach? | Evidence that can inform policy, implementation, and assessment changes. | Learning outcomes that can be adapted to roles, disciplines, and demonstrated needs. |
| What is the main failure if this is done alone? | Rules and review may exist without sufficient understanding or practical skill among users. | People may receive instruction while institutional uses, risks, and effects remain poorly monitored. |
A practical sequence for building both
1. Assign responsibility and set a narrow initial scope
Name the people or bodies responsible for coordinating AI oversight and decide which uses need review. The initial arrangement need not settle every future question; it should make clear who receives concerns, who can convene relevant expertise, and how decisions or guidance are communicated. Include academic, technical, and administrative perspectives where relevant, and provide a route for student participation. UNESCO IESALC specifically recommends governance structures that include students.
Start an inventory of known AI uses across teaching, research, and administration. The inventory is not a claim that every use is equally risky or requires the same approval. Its purpose is to make activity visible enough to identify where more review or support is needed. The OECD’s response areas include compliance and procurement as well as guidance, so a university can consider how existing institutional review processes apply rather than treating AI as separate from its responsibilities.
2. Define a basic review and evidence plan
For each proposed pilot or use selected for review, specify the question the university is trying to answer and the evidence it will consider. The questions should fit the context: a teaching use may raise questions about learning, assessment, privacy, or academic integrity; a research or administrative use may require a different emphasis. Identify potential equity and reliability concerns as well as intended benefits. Decide who will review findings and how they can lead to a change in guidance, implementation, or the decision to continue.
Do not present a pilot as proof that a use is safe or effective everywhere. A pilot supplies evidence about the selected context and the questions it was designed to examine. The OECD calls evidence collection and pilot evaluation emerging system-level responses; UNESCO IESALC recommends monitoring and evaluation. Neither recommendation makes a single pilot a universal certification.
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3. Begin foundational literacy for students and staff
Offer role-appropriate instruction while the governance foundation takes shape. A common foundation can cover AI concepts, limitations, critical evaluation of outputs, responsible use, privacy, integrity, fairness, and the institution’s current expectations. The depth and examples can differ by role: students need to understand how expectations apply to learning and assessment, while staff may need to consider teaching, research, administration, or oversight in their work.
Make clear what is known and what remains under review. If guidance is provisional, explain which current expectations apply and where people can ask questions. Training should not imply that any use is acceptable merely because a person has learned to operate a tool, nor should a policy document be mistaken for evidence that people understand or can apply it. The Council of Europe’s sociotechnical framing is a reminder to include human rights, democracy, social agency, and critical thinking as well as technical and practical understanding.
4. Pilot in selected contexts and review effects
Select a small number of teaching, research, or administrative contexts that are suitable for learning. Before the pilot, state the outcomes and risks to examine: for example, learning or work quality, privacy, equity, integrity, and reliability where those issues are relevant. Gather evidence that can answer those questions, listen for unintended effects, and review results with people who understand the context. This is a practical synthesis of OECD and UNESCO IESALC recommendations, not a validated implementation model or a required universal sequence.
A useful evaluation asks not only whether a task was completed, but what changed and for whom. If an AI-supported teaching activity appears to save time, for example, the institution still needs to consider whether learning goals were met, whether students had fair access to support, and whether assessment expectations were clear. The example describes questions to examine, not a claim that a particular tool or intervention produces those outcomes.
5. Use findings to revise learning and assessment
Review pilot evidence alongside questions from students and staff. Identify where people need additional instruction, where an assessment may need redesign, and where an AI use should be limited, supported, or reviewed further. UNESCO IESALC explicitly connects AI literacy, monitoring, and assessment redesign with clear guidance on AI use. That connection lets curriculum development respond to actual institutional needs instead of relying only on generic tool demonstrations.
Assessment redesign should make expectations understandable: what work learners are expected to do, whether and how AI use is permitted in that context, and how learning will be demonstrated. The exact rules belong to the institution and the course or activity; the cited sources do not prescribe one universal assessment policy. Evaluation findings can help identify where unclear instructions or mismatches between learning goals and assessment need attention.
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6. Scale with local adaptation
Extend approaches that are useful, but adapt them to discipline, role, and institutional mission. A single literacy framework or review procedure may not answer the needs of teaching, research, and outreach equally well. UNESCO IESALC’s 2025 working-paper summary argues that higher-education frameworks should reflect those distinct functions and include knowledge, skills, and attitudes and values.
Do not treat OECD’s 2026 AI literacy framework for primary and secondary education as a university curriculum prescription. Its broad knowledge, skills, and attitudes framing can provide context, but university outcomes need to fit higher education. UNESCO IESALC’s analysis highlights the need for an adaptable framework designed for higher-education institutions, rather than simply transferring a school framework unchanged.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide where to put effort
Universities do not all begin from the same level of readiness. A useful choice is not “governance or curriculum” in the abstract, but which gap is most pressing while maintaining a minimum effort on the other side. The following comparison criteria are synthesized from the cited recommendations; they are not a published or validated scorecard.
Readiness and risk coverage
Ask whether the institution can detect and respond to relevant privacy, integrity, reliability, and equity issues while training is being developed. If uses are already present but no one can tell where they occur or how concerns are handled, the initial governance foundation deserves urgent attention. That does not remove the need for basic literacy, especially clear expectations for current use.
Evidence quality and feedback speed
Ask whether pilots and monitoring can show which uses help or harm learning and operations. If decisions are being made without evidence about effects, define a small set of review questions before scaling. Keep the questions appropriate to the use; evidence about one course or administrative workflow does not automatically establish results in another.
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Ask whether the institution can support students and staff across departments, and whether some groups may be left behind. UNESCO IESALC reports resource constraints and differences between private and public institutions in its Latin American and Caribbean sample. That is a reason to examine local capacity and access, not to assume the same pattern or solution applies at every university.
Curricular breadth and assessability
Check whether literacy covers knowledge, applied skills, critical judgment, and values, and whether learners can demonstrate those capabilities in appropriately designed assessments. A curriculum that covers only tool operation would miss the critical and ethical dimensions identified by UNESCO IESALC and the sociotechnical framing emphasized by the Council of Europe.
What the evidence can—and cannot—establish
The sources make a strong case for treating AI governance, evaluation, and literacy as connected institutional responsibilities. They do not establish a universal timetable, a fixed staffing model, or a single curriculum that every university should adopt. The recommendation to build a minimum review foundation alongside early literacy is a practical way to act on that combined guidance while generating local evidence.
The 2025 UNESCO IESALC working-paper summary diagnoses fragmented institutional responses and the absence of a comprehensive, adaptable AI competency framework specifically for higher education. It also discusses figures on student preparedness and faculty use drawn from the paper’s literature review. Those figures should not be treated as universal estimates without checking the original studies. Separately, a 2026 UNESCO IITE teacher AI competency report analyzes 36 national, institutional, and academic frameworks. That is a count of frameworks reviewed, not an outcome statistic or proof that one framework is more effective.
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Universities should use these sources as policy and framework guidance, while making local decisions based on their own missions, resources, and evidence. A regional adoption finding, a framework review, and a policy recommendation answer different questions; none alone shows that a particular sequence will work for every institution.
Frequently Asked Questions
Does an AI evaluation layer have to come before an AI literacy curriculum?
No. The reviewed sources recommend governance, evaluation, competency development, and curriculum work together; they do not prove that evaluation must always precede literacy. A sensible starting point is a minimum review and governance foundation alongside early, role-appropriate instruction, then use pilot evidence to refine both.
Are the UNESCO IESALC AI adoption percentages global university estimates?
No. The 2026 study covers 200 institutions across 19 countries in Latin America and the Caribbean. Within that sample, 87% reported AI use in at least one area, 74% reported use in teaching and learning, and 26% had a formal AI strategy. These figures should not be generalized as global estimates.
Can a university use a school AI literacy framework as its curriculum?
Not without adaptation. OECD’s 2026 AI literacy framework is for primary and secondary education, so it is useful for broad competency language rather than a university-specific prescription. Higher-education outcomes should reflect university roles and missions, including teaching, research, and outreach.
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Do the sources recommend buying a particular AI tool or product?
No specific physical product or vendor is established by the reviewed sources. The OECD includes specialized higher-education AI tools among emerging response areas, and the research supports considering faculty and staff competency training, but it does not identify a recommended vendor or establish affiliate availability. Any provider should be assessed for university relevance and program terms.
The Bottom Line
Build the minimum governance and evaluation layer while launching foundational AI literacy. Use pilots and monitoring to learn what works, address risks, and adapt curriculum and assessment to local evidence—not to impose a universal sequence the research has not proven.
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