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Why can’t one university office evaluate every AI tool?
An AI tool can be technically safe enough to operate and still be unsuitable for a particular course, research method, or assessment. The reverse is also true: a promising academic use does not answer whether the tool collects sensitive information, fits campus systems, or creates unacceptable security or privacy risks. Universities therefore need more than one kind of expertise before deciding how a tool may be used.
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The decision may also affect people beyond the person who first wants to try the tool. A service used in a class can involve student work and records; one adopted by a research group can touch research data; a tool used by staff may process institutional information. The relevant questions and consequences depend on the proposed use, not just the product name.
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What should faculty, IT, and a committee each do?
| Participant | Primary contribution | Questions to answer | What this role does not establish by itself |
|---|---|---|---|
| Faculty, academic departments, and teaching and learning centers | Academic and disciplinary value, learning outcomes, assessment, academic integrity, and research practice | Does the tool serve the intended course, discipline, or research task? Could its use undermine a learning objective or create an assessment problem? | Whether the tool’s data handling, security, integration, or support arrangements are acceptable |
| IT, security, privacy, and data governance teams | Technical operation and institutional risk | What information does the tool receive or retain? What security, privacy, integration, accessibility, and support issues need attention? | Whether the tool is pedagogically sound or appropriate for a specific discipline |
| Cross-functional AI committee or working group | Shared criteria, consultation, coordination, and recommendations on cross-campus effects | Does the proposal affect multiple units? Are institutional rules consistent? Which body has authority to act? | Formal approval, unless that authority has been explicitly delegated |
| Designated leaders and established governance bodies | Formal policy, budget, procurement, risk acceptance, academic governance, and accountability according to local authority | Who can authorize use, set conditions, or reject a high-risk proposal under existing processes? | Specialist analysis that should inform the decision before it is made |
This is a functional allocation, not a claim that every institution has all these units or that each decision belongs to one named executive. Depending on local rules, authority may sit with a provost, CIO, research leadership, president or cabinet, faculty senate, procurement body, or another established office. The institution should map actual decision rights rather than assume a new AI committee supersedes existing governance.
Is a central AI committee the approver?
Not automatically. A committee is valuable because it can bring academic, technical, privacy, security, and operational perspectives together, identify common concerns, and recommend shared criteria. Its existence does not, by itself, give it authority to approve purchases, accept risk, set academic policy, or speak for a faculty senate or university administration.
The University of California, Santa Barbara’s joint Senate-administration AI Advisory Committee illustrates the distinction. Its report says the committee “does not have decision-making authority nor does it speak for the Academic Senate or the university administration.” It referred recommendations to campus bodies able to act through their own processes. The practical lesson is to state in the committee charter whether members advise, coordinate, approve within a defined remit, or escalate proposals to another body.
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EDUCAUSE’s framework separates governance, operations, and pedagogy, reinforcing why coordination and authorization need not be the same function. A committee can own the process of assembling evidence and making a recommendation while the appropriate leaders or governance bodies make the consequential decision.
How do the three governance approaches compare?
| Approach | Strength | Risk if used alone | Best role in a shared process |
|---|---|---|---|
| Faculty-led | Close understanding of disciplinary practice, learning aims, and assessment | May not resolve privacy, security, integration, procurement, or institution-wide consistency questions | Evaluate academic purpose and conditions for use in courses or research |
| IT-led | Strong view of technical operation, infrastructure, integration, and security concerns | Technical acceptability does not show that a tool advances learning or suits a discipline | Review technical risk and operational requirements |
| Committee-led | Can coordinate perspectives and reduce conflicting rules across units | Can blur accountability or add delay if its authority, remit, and escalation route are unclear | Set common criteria, consult relevant specialists, and recommend action to authorized bodies |
| Shared evaluation with named decision authority | Combines academic and technical assessment while keeping accountability visible | Requires deliberate coordination and clear communication | Use as the institution’s operating model, with authority assigned according to existing governance |
There is no controlled head-to-head evidence establishing that one committee structure is universally faster or more effective. The comparison is about the distinct contributions and failure modes of each approach. A university should choose arrangements that fit its size, existing governance, and risk profile, then make the route understandable to the people who need to use it.
How should a university build an AI tool review process?
- Define remit and decision rights. Name the office or body accountable for institutional policy. State what a review group may decide and what it may only recommend. Identify who can set conditions, accept residual risk, reject a proposal, or hear an appeal or exception request.
- Review a proposed use, not only a product. Ask who will use the tool, for what task, in which setting, and with what information. A tool’s proposed classroom use may raise different questions from a staff workflow or a research application. Record the intended use and affected users so reviewers assess the actual case.
- Use proportionate review. Published guidance may be enough for routine, low-risk teaching or productivity uses. A tool handling sensitive information, supporting a core research system, or informing a high-impact decision warrants deeper specialist review. This tiered approach is a practical recommendation, not a tier model prescribed by the cited guidance.
- Bring in both academic and technical reviewers when relevant. A technical review cannot decide whether the use serves a learning objective. An instructor’s judgment cannot settle institutional data-protection or security questions. Route each proposal to the specialists who can answer its material questions, then connect their assessments before a decision is made.
- Document the decision and its conditions. Keep a record of the proposed purpose, affected users, data involved, identified risks, conditions of use, responsible owner, decision-maker, and date to revisit the decision. This is a practical recordkeeping recommendation, rather than a quoted requirement from a specific source.
- Publish the outcome and provide a route for questions. Explain approved uses, restrictions, support, and how to request review or an exception. Staff and faculty need a visible place to check guidance instead of relying on rumor or an unclear approval chain.
- Reopen decisions when circumstances change. Reconsider a decision when a tool changes materially or someone proposes a new use. UNESCO’s education guidance calls for monitoring and validation, and the Commission emphasizes the need for institutions to remain nimble as AI evolves.
What should reviewers examine?
Academic purpose and consequences
Start with the problem the user is trying to solve. Faculty and relevant academic units should determine whether the tool fits the discipline, course, research practice, and intended learning outcomes. They should consider how its use affects assessment and academic integrity, including whether a student or researcher could meet a task’s stated objective while relying on the tool in a way that changes what is being assessed. The evaluation should lead to a clear account of the permitted purpose and any academic conditions, not a blanket assumption that one tool is suitable for every class or field.
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Data, privacy, security, and operations
Technical and data specialists should establish what information is submitted, how it is handled, and what institutional exposure follows. They should consider security, privacy, integration, accessibility, and support needs. The review should make clear which data or workflows require restrictions and who can answer operational questions. The supplied sources support examining these domains, but do not establish a universal checklist of contractual or technical controls; institutions should use their own applicable policies and expertise.
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Consider whether the proposed use is confined to one course or unit, or could affect multiple departments, shared systems, policy, budgets, or institutional risk. Cross-campus consequences make coordination especially important. The recommendation should identify an accountable owner and the body with authority to act, so a favorable technical or academic assessment is not mistaken for complete institutional approval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a university keep review workable and transparent?
Review should be proportionate to the use and risk. EDUCAUSE’s 2025 Horizon Report panel warns that lengthy or burdensome approval processes can discourage experimentation or push users toward workarounds. A process that is too light, however, can leave users without guidance on institutional exposure or academic expectations. Published guidance for routine uses, with a clear route to deeper review when needed, is a way to avoid treating every proposal as either automatically approved or a full-scale exception.
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Communication is a governance function, not a final announcement. EDUCAUSE’s 2026 report found that 94% of respondents had used AI tools for work in the preceding six months, while 54% said they were aware of policies and guidelines for that use. These are self-reported survey findings, not an audit of compliance or policy effectiveness, but they show why a university should make current rules easy to find and explain how to get help.
The same EDUCAUSE report was based on 1,960 eligible responses; the survey was disseminated from September 29 to October 13, 2025. Respondents reported the following units as having decision-making authority for work-related AI policies and guidelines:
| Reported unit with decision-making authority | Share of respondents |
|---|---|
| Institution-wide leadership | 59% |
| IT | 53% |
| AI governance unit or committee | 38% |
| Data governance unit or committee | 29% |
| Respondent’s own work unit | 17% |
| Human resources | 9% |
| Other | 9% |
| Don’t know | 3% |
These figures are reported perceptions about work-related policy and guideline authority. They are not a universal allocation of governance, and they do not directly measure who procures or approves every academic tool. The spread across leadership, IT, committees, and individual work units is a reason to clarify local authority rather than to copy a percentage-based org chart.
What should an AI committee charter make explicit?
A short, usable charter should remove the ambiguity that causes delays and misplaced expectations. It should identify:
- Which proposals fall within the group’s remit and which remain with existing academic, procurement, research, or operational processes.
- Whether the group makes decisions or recommendations, and which officer or body has final authority for each type of decision.
- Which expertise must be consulted for academic, security, privacy, data, accessibility, and operational questions.
- How a user submits a proposal, what information is needed, how urgent or high-risk cases are escalated, and where exceptions are considered.
- Where current guidance and decisions are published, who maintains them, and when or under what circumstances a decision is reviewed again.
This is not a substitute for an institution’s legal or governance requirements. The available guidance supports an institution-wide, cross-functional approach, but does not establish one legally required structure for every university or jurisdiction.
What is the practical answer to “Who approves AI tools?”
There is no single answer that applies to all universities. Faculty should judge academic fit; IT and related specialists should judge technical and institutional risks; a cross-functional committee should connect the evaluations and recommend consistent action. The university’s designated leaders and established governance bodies should make or authorize formal decisions within their local responsibilities. A good process tells people which of those steps applies to their intended use, what evidence is needed, and who can make the call.
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