AI Governance for Education

AI governance in education should protect the distinction between improving a learning service and judging an individual learner. An assistant that summarises module feedback can help course teams identify problems, but it should not quietly become a system for inferring a student's wellbeing, academic ability or disciplinary risk. Those uses have different purposes, consequences and accountable decision makers.

For Saudi universities, colleges and education providers, the practical task is to define the educational purpose, permitted evidence, responsible academic authority and student-facing route for questions or correction. This guide uses a fictional university feedback summariser. It provides an operating design for institutional leaders, academic quality teams, student services and technology owners, rather than a grading method or a clinical assessment tool.

Define the educational decision before approving the tool

Start with what the institution intends to improve. Summarising recurring concerns about a module timetable, helping a lecturer prepare materials and deciding whether a learner needs individual intervention are different activities. A generic approval for “AI in education” does not settle which of them is appropriate. Give each use case a purpose statement, audience, output type and set of excluded decisions.

For a feedback summariser, an appropriate initial purpose might be to prepare a reviewable account of themes for the module team. Identify who can read the original comments, who receives the summary and whether any output enters an individual student record. If individual profiling is outside scope, that exclusion should be enforceable in data access and product configuration, not merely written in guidance.

UNESCO's guidance on generative AI in education emphasises human agency, privacy and age-appropriate approaches to validation and educational design. It is an international guidance resource, not a Saudi legal approval for a particular tool. Use it to frame institutional questions while separately determining applicable Saudi requirements and the provider's own academic obligations. Read the UNESCO guidance.

Assign academic authority and student-service responsibility

Different educational consequences belong to different owners. An academic quality team may govern module improvement; an authorised academic body owns grading rules; student services controls its support processes; and the information or privacy owner assesses data use. The technology team implements the approved capability. A vendor cannot settle an academic purpose simply by making a feature available.

Document who resolves disagreements. A department may want detailed comments to understand an issue while the privacy owner identifies a risk of revealing a respondent in a small cohort. The decision needs an authorised route that can adjust granularity, audience or workflow. It should not depend on whichever team has administrative access to the dashboard.

Suggested purpose and authority boundaries

Use	Accountable educational owner	Key boundary
Aggregate module feedback	Academic quality or course owner	Improve the module without creating student profiles
Draft learning materials	Responsible educator	Review content for the intended curriculum and learners
Support outreach to an individual	Authorised student-support process	Separate purpose, evidence and access assessment
Assessment or grading	Relevant academic authority	Apply approved assessment rules and review routes
Safeguarding or urgent concerns	Designated specialist process	Follow established procedures rather than relying on a generated diagnosis

Use the institution's decision-rights framework to name the owners and escalation route. This table is a proposed operating distinction, not a statement that every institution uses the same committee titles.

Worked example: feedback themes become individual wellbeing labels

Imagine a university approves an assistant to summarise anonymous module feedback for course improvement. A supplier update introduces a feature that estimates individual wellbeing from comment language. The project team suggests linking the result to student identifiers to enable earlier support. That proposal changes the purpose, identifiability, output meaning and potential consequences for the student.

The original feedback approval does not authorise the new use. The institution should decide whether the proposal is appropriate at all, who has competence to assess it, what evidence would be needed and which information could lawfully and properly be used. Until that decision is complete, keep the summariser within its approved aggregate purpose and disable the individual-label feature.

Evaluate the original tool using 24 synthetic feedback sets: eight representing small cohorts, six containing sarcasm or ambiguous tone, four mixing Arabic and English, four containing sensitive disclosures and two naming a lecturer. These groups are an illustrative test design, not real student records, a minimum legal sample or a measure of clinical validity.

The expected result is a faithful, reviewable account of relevant themes with appropriate handling of identifiable or sensitive material. The tool should not turn an ambiguous sentence into a diagnosis, reveal a likely respondent or erase a minority concern merely because most comments are positive. Review the summary with the original set available to authorised staff.

Preserve the difference between anonymity and missing names

Removing names does not necessarily make feedback anonymous in practice. A comment may identify its author through a rare placement, a distinctive event or a small group. The governance design should assess the combination of content, audience and contextual knowledge. A lecturer who knows the cohort may recognise a person from detail that appears harmless to a technology team.

Set rules for small-group reporting, quotation and access with the responsible information owner. An internal minimum group size can be a useful control but is not a universal guarantee of anonymity. Consider whether to combine themes, reduce detail, delay reporting or route a particular comment through a restricted process. Record the reason for the chosen treatment rather than applying a numerical threshold without context.

Test the summary itself for disclosure. Even when the model does not reproduce a name, it may combine details from several comments into an identifiable story. Reviewers should be able to remove unnecessary details while preserving the educational issue. The PDPL governance process can support the wider purpose, access and information-rights decisions.

Evaluate Arabic meaning, minority views and uncertain tone

Arabic feedback may include informal phrasing, mixed-language terms, indirect criticism or humour. Design evaluation around meaning rather than a simple positive-or-negative score. A sentence praising the difficulty of a course may express satisfaction, while a superficially positive phrase may be sarcastic. Where interpretation is uncertain, the summary should preserve that uncertainty or describe the concrete issue without assigning a confident emotional label.

Check whether English comments disproportionately shape the summary when most feedback is Arabic, or whether a translated label changes the strength of criticism. Ask bilingual academic reviewers to compare outputs against the source sets. Record material omissions, exaggerations and misleading translations separately so the team knows what needs improvement.

Preserve important minority themes without making prevalence claims the evidence cannot support. A single accessibility concern may deserve attention even if it is not common. At the same time, one comment should not become “students generally report”. Give reviewers enough information to distinguish a recurring theme, an isolated but consequential concern and an ambiguous statement that needs interpretation.

Make course improvement traceable without exposing respondents

Link an approved theme to an action owner and review point. If feedback identifies unclear assignment instructions, the course team can review the instructions and report the resulting improvement. The action record needs the educational issue and supporting basis; it may not need to reproduce a student's entire comment or disclose who raised it.

Separate the restricted evidence store from the improvement tracker. Authorised reviewers may need original comments to verify the summary, while a wider departmental group may only need the approved theme and action. Define which record is retained, who can access it and how corrections propagate. Avoid copying raw feedback into every meeting pack simply because it is easy to export.

Close the loop with learners through the institution's approved communication channels. Explain what was heard, what can change and what requires further review. Do not present an AI-generated theme as the voice of the entire cohort without qualification. A useful governance outcome is that students can see how feedback informed an accountable decision, while individual contributors remain appropriately protected.

Treat sensitive disclosures through existing specialist routes

A feedback channel can contain material outside its intended educational-improvement purpose. Decide how authorised staff handle a disclosure suggesting an urgent concern or a need for specialist support. The assistant should not be asked to diagnose the student or invent a response protocol. The institution's designated safeguarding, wellbeing or other relevant process determines how the matter is assessed and handled.

Define the operational handoff before deployment. Who receives a restricted alert? What source context can they inspect? How is access controlled? How do staff avoid leaving an important concern in an unmonitored summary queue? The decision should reflect the channel's design and the information available, including whether a respondent can be identified or contacted appropriately.

Test the route using synthetic material and trained staff. The exercise should establish that the right process can receive a concern without broadcasting it to the whole course team. Record limits clearly: a feedback summariser is not an emergency service, and its output cannot establish a clinical conclusion. Communications about the channel should match its actual purpose and support arrangements.

Keep assessment and academic integrity decisions separate

Do not allow feedback labels to flow into grading, disciplinary or academic-integrity records by default. If the institution proposes a new use involving individual judgments, assess that use independently with the relevant academic authority. The original data collection purpose, expected audience and quality of evidence may not support it.

For any authorised assessment-related AI, define the substantive decision rule, evidence the reviewer can inspect and the student's existing route to question a result. A model score is not self-explanatory. Review whether staff understand the output's limitations and whether the process permits correction before a consequential decision is finalised.

The practical boundary should appear in interfaces and data flows. A feedback report should not export individual risk columns simply because another system can ingest them. Test for accidental joins, inherited permissions and reusable labels that make a new purpose possible without a formal change request. Connect new uses to AI change control.

Evaluate educational benefit beyond administrative speed

Measure whether the tool produces useful, faithful themes and helps the responsible team act. Possible indicators include material theme omissions, unsupported generalisations, disclosure corrections, bilingual review effort and completion of agreed improvement actions. Define the denominator and the observation period. Faster summarisation is valuable only in relation to the quality and consequences of the resulting decision.

Compare equivalent tasks. If one cycle contains fewer comments or a simpler issue mix, a shorter turnaround may not be attributable to AI. Include the time required to check, correct and handle exceptions. Ask educators whether the summary directs attention to evidence they would otherwise miss, rather than merely producing a more polished version of what they already knew.

Review student-facing consequences as well. An inaccurate summary can misrepresent a concern or undermine confidence in the feedback process. Provide a route for an issue to be reconsidered without requiring a learner to understand the model. The institution remains responsible for the improvement decision it communicates.

Specify supplier permissions and educational data use

Ask the supplier to identify the data it receives, the purposes for which it uses that data, any model-improvement use, support access, subprocessors and retention arrangements. Determine the appropriate requirements with procurement, legal and privacy owners. A contract label such as “education edition” does not establish that every institution's intended use is acceptable.

Demonstrate the approved configuration. Can the institution disable individual inference, prevent unintended exports and restrict who reads raw comments? Can it obtain evidence of configuration changes and withdraw a capability after an update? A supplier's product roadmap should not become the institution's de facto academic policy.

Plan the end of the service. Preserve approved improvement records and the evidence required under institutional rules, while handling other material according to the applicable arrangements. Confirm who can retrieve records and who validates deletion or transfer where required. Use third-party risk questions to maintain these responsibilities after procurement.

Run governance with educators and students in the loop

Include educators and appropriate student representatives in testing the meaning and usability of the process, while protecting access to confidential material. Their role is to expose misleading summaries, unclear communications and burdens the project team may overlook. Participation should be structured around realistic tasks rather than a general question about whether people like AI.

Give staff an accessible route to report an output that feels wrong or a feature that exceeds its purpose. Assign someone to inspect the source, decide whether an output needs correction and identify related cases. Repeated concerns should inform the risk record and release decision, not remain scattered across informal messages.

For a material incident, connect academic, student-service and technical responses through incident handling. Preserve a usable alternative process so feedback and support do not depend entirely on a disputed AI feature. Restart only the capability whose purpose and evidence have been reconsidered.

Preserve an academic disagreement that the summary cannot settle

Feedback can reveal competing preferences rather than a single correct improvement. Some students may want more guided examples while others value independent work. The assistant should describe the supported difference without inventing a consensus. The academic owner decides how the course objectives, resources and evidence inform the response.

Use a decision record that distinguishes the feedback theme, the educational judgment and the resulting action. A course team may retain an approach while improving its explanation, or change one element without adopting every request. Record the reasoning at the appropriate level and communicate it through the approved process. This keeps the model from becoming an invisible arbiter of educational design.

Test whether the summary preserves disagreement when one view is expressed more fluently or in a different language. Reviewers should be able to correct an imbalance without reconstructing the entire dataset. The evaluation question is faithful representation of the evidence, not whether the assistant produces a convenient recommendation that eliminates debate.

Prepare a purpose-specific education governance pack

The handover should include the educational purpose, excluded individual decisions, authority map, data and audience boundaries, small-cohort reporting rules, bilingual evaluation, specialist handoff and correction process. Add supplier settings and change triggers that would require a fresh review. Each element needs a continuing institutional owner.

Demonstrate the design with a difficult feedback set rather than a clean sample. Ask the academic owner to identify what can be concluded, the privacy owner to explain what may be shared and the service owner to show how a serious concern reaches the appropriate process. Their answers should agree with what the technology permits.

For broader application context, see Trusted's education AI overview. To establish the operating structure, explore AI governance setup with OpenForAI and discuss the educational purpose, student protections and decision boundaries for your institution.