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Universities should establish a small AI governance and evaluation foundation while beginning foundational AI literacy—not wait for one program to be finished before starting the other. The evaluation work helps the institution identify risks, test uses and learn what needs to change; literacy helps students and staff use and assess AI critically and responsibly. Evidence from pilots can then inform curriculum and policy. This is a practical sequencing recommendation, not a proven rule that every university must follow in the same order.
What an AI evaluation layer and an AI literacy curriculum do
An AI evaluation layer is the institution’s recurring process for identifying AI uses, reviewing them, collecting evidence, running pilots, monitoring effects and risks, and adjusting policy or implementation. “Evaluation layer” is an operational label for that connected work, not the name of a standardized framework. It should make it possible to consider academic integrity, data protection, equity, reliability, and how AI use affects student knowledge and skills.
An AI literacy curriculum develops the knowledge, skills, and attitudes people need to understand and use AI critically and responsibly. In higher education, that means more than learning how to operate a tool. It can include understanding AI concepts and ethical issues; using, evaluating, and critically interacting with AI; and applying values such as fairness, accountability, transparency, and equity. The Council of Europe’s 2026 recommendation takes a sociotechnical view, linking human, technological, and practical dimensions. Its announcement puts the point plainly: “AI literacy, on this view, means understanding AI’s effects on society, not just knowing how the tools work.”
The two efforts have different outputs. Evaluation produces institutional knowledge and decisions about particular uses. Literacy develops learner and staff capability. A university needs both because sound policies cannot by themselves teach people how to judge AI output, while training alone cannot give the institution a dependable way to identify and respond to risks.
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Which should a university start first?
Start a minimum governance and evaluation foundation first or in parallel with foundational literacy. Do not make a full, university-wide curriculum a prerequisite for reviewing AI use, and do not postpone basic instruction until pilots are complete. The initial evaluation foundation should be small enough to start quickly but sufficient to assign responsibility, identify uses that need review, and establish how pilots will be assessed. Early literacy should explain current expectations and the limits and risks relevant to learners’ roles.
This sequencing is a synthesis of recommendations from the OECD’s 2026 policy paper and UNESCO IESALC’s higher-education analysis, not a causal finding. The reviewed sources recommend governance, evaluation, competency development, and curriculum work as connected responses; they do not establish that evaluation must always precede curriculum. A university with an existing AI governance process can use it rather than create a duplicate structure. One with urgent student or staff needs can begin basic literacy immediately while it establishes review procedures.
The practical reason to put a minimum evaluation function near the beginning is that it gives the institution a way to learn from real use and to respond while broader training is being developed. The reason to begin literacy at the same time is that students and staff need clear, role-appropriate guidance about responsible use now—not only after the institution has completed its policy design.
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The OECD’s 2026 policy paper describes generative AI’s effects across learning, student support, teaching, assessment, skills, research, and institutional administration. It identifies data protection, academic integrity, equity, reliability of generated content, and students’ knowledge and skills as material challenges. Its five emerging system-level response areas are responsible-use guidance; compliance and procurement; competency development; evidence collection and pilot evaluations; and specialized AI tools for higher education. That agenda treats evaluation and competency development as complementary work, not alternatives.
UNESCO IESALC’s 2026 regional study illustrates why governance capacity matters, but its figures must be read within their scope: it draws on 200 higher-education institutions across 19 countries in Latin America and the Caribbean. In that sample, 87% of institutions 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 were even less common. These are findings for the study’s regional sample, not global estimates.
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UNESCO IESALC recommends comprehensive AI strategies with governance from the outset, dedicated governance structures that include students, AI literacy as a graduate competency, broader training, monitoring and evaluation, and assessment redesign with clear guidance on AI use. Those recommendations connect the institutional and educational sides of the problem: a university needs to know how AI is being used and what effects it has, while preparing people to make informed decisions within that environment.
A 2025 UNESCO IESALC working-paper summary describes institutional responses as fragmented and identifies a lack of a comprehensive, adaptable AI competency framework specifically for higher education. It argues that university frameworks should reflect teaching, research, and outreach, and encompass knowledge, skills, attitudes, and values. The summary includes figures about student preparedness and faculty use drawn from its literature review; without checking the original studies, those figures should not be generalized across institutions.
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How the two efforts differ in practice
| Question | Evaluation and governance | AI literacy curriculum |
|---|---|---|
| Primary purpose | Identify uses, review risks, test implementation, collect evidence, and guide institutional decisions. | Develop the knowledge, applied skills, critical judgment, and values people need to engage with AI. |
| Typical focus | Specific uses and their effects on learning, assessment, research, administration, privacy, equity, and reliability. | How AI works and where it is limited; how to use and assess it; and how to act responsibly in context. |
| Who needs to contribute | Relevant academic, technical, administrative, and student perspectives; UNESCO IESALC specifically recommends student representation in governance structures. | Students and staff, with learning shaped to their roles, disciplines, and institutional context. |
| Evidence of progress | Documented reviews, pilot evidence, monitoring, and decisions to change, limit, or continue a use. | Learners can demonstrate understanding, practical judgment, and responsible use—not simply report that they attended training. |
| How it informs the other | Findings can reveal where guidance, training, assessment, or safeguards need to change. | More capable users can apply guidance and make more informed choices; curriculum should reflect risks and needs identified in practice. |
The table describes a practical distinction, not a formal scorecard. A university should not treat a completed inventory or a one-off training session as proof that it has solved the underlying problem. The value comes from a repeatable cycle in which the institution learns, communicates expectations, and revises its approach when evidence or conditions change.
A practical sequence for universities
1. Assign responsibility and define the first review boundary
Name a responsible group or existing governance body and specify who can make decisions, who must be consulted, and how issues are escalated. Include academic, technical, and administrative knowledge, and involve students where their learning or data is affected. UNESCO IESALC specifically recommends dedicated governance structures that include students. The goal at this stage is not to review every imaginable AI use; it is to ensure the university can see and respond to uses that matter.
Start an inventory of AI uses that are already in operation or proposed. For each use, record its setting, purpose, people affected, data involved, and the person or unit responsible. These are practical fields a university may choose to track, not a prescribed UNESCO or OECD template. The inventory helps identify where a more detailed review or pilot is warranted and reduces the chance that an important use remains invisible simply because it sits outside a central project.
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2. Set basic expectations and launch foundational literacy
Publish clear, accessible guidance while broader policy is being developed. It should explain the institution’s current expectations and point people toward help when a use is uncertain. Alongside that guidance, begin foundational learning for students and staff. Cover AI concepts and limitations, critical assessment of generated output, privacy, academic integrity, fairness, and responsible use. Make expectations specific enough to help people act, while distinguishing requirements that are settled from areas still under review.
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Literacy should match roles. Students need to understand the rules for AI use in their courses and how to evaluate output; instructors need to make expectations and assessment design clear; researchers and administrators may face different questions about data, reliability, and the purposes of a system. The Council of Europe’s sociotechnical framing also cautions against reducing literacy to tool operation: human rights, democracy, social agency, and critical thinking belong alongside technical and practical understanding.
3. Select a small number of pilots and define questions in advance
Choose a manageable set of teaching, research, or administrative contexts rather than trying to assess every use at once. Before a pilot begins, write down the questions it is meant to answer. Depending on the context, these may concern learning, quality, privacy, equity, academic integrity, reliability, or operational effects. Decide what evidence would be relevant and how unintended effects will be noticed. OECD identifies evidence collection and pilot evaluation as system-level responses; UNESCO IESALC recommends monitoring and evaluation as part of institutional implementation.
A pilot is not automatically evidence of benefit just because a tool is used or participants report liking it. The institution should examine the purpose of the use and the effects that matter in that setting. For example, a teaching pilot should consider whether the intended learning is supported and whether assessment still gives students a fair way to demonstrate their own knowledge and skills. A review should also consider who may be disadvantaged or exposed to risk, rather than looking only at average or convenient outcomes. These are practical evaluation questions, not a claim that the cited sources prescribe one universal measurement method.
4. Review findings and change the curriculum, policy, or use
Use pilot and monitoring findings to decide what should happen next: continue a use with safeguards, revise it, provide additional training, redesign an assessment, restrict a use, or stop it. Record the rationale and communicate changes to the people affected. If learners repeatedly misunderstand permitted use, the response may require clearer course guidance or instruction. If an assessment no longer measures the intended knowledge or skills, redesign may be more appropriate than relying on a rule alone. If a use creates risks the institution cannot adequately address, the institution should not treat literacy as a substitute for safeguards.
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This feedback step is what makes the evaluation layer useful to curriculum planning. UNESCO IESALC links literacy, monitoring, and assessment redesign; the OECD identifies competency development and evaluation as parallel responses. Training should therefore be adjusted based on observed needs, rather than treated as a fixed package independent of actual uses.
5. Scale with local adaptation
Expand the approach once the institution has learned which responsibilities, review questions, and learning outcomes work in its setting. Adapt by discipline, role, and institutional mission. Universities differ in their teaching, research, and outreach, and the same AI use can carry different stakes in different contexts. Scaling should carry forward evidence and useful practices, not assume that a single policy statement or course meets every department’s needs.
Be careful when borrowing frameworks from other education levels. The OECD’s 2026 AI literacy framework is explicitly aimed at primary and secondary education. Its broad concepts of knowledge, skills, and attitudes can offer context, but it is not a university-specific curriculum prescription. UNESCO IESALC’s higher-education analysis emphasizes the need for approaches fitted to higher-education institutions and their teaching, research, and outreach functions.
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Universities do not need to choose between a complete governance program and a complete curriculum before acting. They can use four questions to identify the next practical investment. These are comparison criteria synthesized from the recommendations above, not a published or validated scoring system.
- Readiness and risk coverage: Can the institution identify and respond to privacy, integrity, reliability, and equity concerns while training is being developed? If not, establish responsibility and a basic review route. If expectations are already clear but users lack the skills to apply them, prioritize role-appropriate literacy.
- Evidence quality and feedback speed: Can a pilot or monitoring process tell the institution what is helping or harming learning and operations? If not, define a few meaningful questions and a way to review results before expanding use. If evidence already exists, use it to target curriculum and policy changes.
- Implementation capacity and equity: Can support reach learners and staff across departments, including less-resourced groups? UNESCO IESALC reports resource constraints and differences between private and public institutions in its Latin American and Caribbean sample. A plan that assumes equal capacity everywhere may leave some groups without either useful training or a way to raise concerns.
- Curricular breadth and assessability: Does literacy cover concepts, applied skills, critical judgment, and values, and can learners demonstrate those capabilities through appropriate assessments? If it covers only tool operation, broaden it. If expectations cannot be assessed fairly, clarify what students should learn and redesign the assessment as needed.
These questions help distinguish a genuine bottleneck from a preference for one kind of program. A university that lacks any route for reviewing AI uses has a governance gap; one that has policies but no meaningful preparation has a literacy gap. Many institutions will have both, and the initial response should be sized to the risks and resources at hand.
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Common sequencing mistakes to avoid
Waiting for a perfect policy before teaching anything
Foundational literacy can begin before a university has resolved every policy question. Teach stable principles and communicate current expectations, while being honest about points still under review. Otherwise, learners may be left to infer the rules from inconsistent local practice.
Launching training without a way to learn from use
A curriculum cannot tell the institution by itself whether a particular deployment is reliable, equitable, or appropriate. Without inventory, review, and monitoring, the university may miss where a use is occurring or what effects need attention. Training and governance solve different problems.
Counting activity instead of capability or effect
Attendance at a session, number of AI tools in use, or completion of a policy document does not alone show that learners can critically assess AI or that a use improves an outcome. Evaluation should return to the questions defined for the setting; literacy should be demonstrated through learning and judgment appropriate to the learner’s role.
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A framework designed for another level of education or institutional setting may not fit university teaching, research, or outreach. Use broad competency concepts as reference points, but adapt outcomes and review practices to local responsibilities, disciplines, and mission.
What the available evidence can—and cannot—settle
The evidence supports treating evaluation, governance, competency development, and literacy as a connected agenda. OECD’s 2026 paper names both evidence collection and competency development among higher-education responses. UNESCO IESALC recommends governance, training, monitoring, AI literacy, and assessment redesign. The Council of Europe’s 2026 framing broadens literacy beyond technical familiarity. Together, these sources support beginning a minimum evaluation foundation alongside early literacy and improving both as the institution learns.
They do not establish that a particular sequence is universally superior, quantify the effect of starting evaluation before curriculum, or provide a validated institutional scorecard. Nor should the UNESCO IESALC regional adoption figures be presented as global rates. The sound decision is therefore conditional: establish the minimum capacity needed to identify and review consequential uses, start baseline learning promptly, and use local evidence to determine what to scale or change.
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