Educational AI Procurement and Student Data Decision Gate

Public field comparison

Educational AI Procurement and Student Data Decision Gate

Educational AI Procurement and Student Data Decision Gate

Evaluate an educational AI purchase against learning need, evidence, accessibility, privacy, security, equity, retention, lock-in and total cost before an accountable decision.

Evaluate an educational AI purchase against learning need, evidence, accessibility, privacy, security, equity, retention, lock-in and total cost before an accountable decision.

Use this when a learning institution needs a decision-ready evidence pack for an AI product that may process student or teaching data.

Use this when a learning institution needs a decision-ready evidence pack for an AI product that may process student or teaching data.

Educational AI Procurement Gate
Student Data Vendor Review
Learning Technology Renewal Decision

Educational AI Procurement Gate
Student Data Vendor Review
Learning Technology Renewal Decision

learning_need_and_use_case
vendor_and_commercial_evidence
data_and_control_evidence
institutional_requirements

learning_need_and_use_case
vendor_and_commercial_evidence
data_and_control_evidence
institutional_requirements

Run this prompt in any capable AI assistant after removing personal and commercially restricted information that is not authorized for the selected tool. Paste the learning need and attach permitted proposal, contract, privacy, security, accessibility and pricing evidence. Have procurement, finance, privacy, security, accessibility and academic owners review their sections before any decision.

Run this prompt in any capable AI assistant after removing personal and commercially restricted information that is not authorized for the selected tool. Paste the learning need and attach permitted proposal, contract, privacy, security, accessibility and pricing evidence. Have procurement, finance, privacy, security, accessibility and academic owners review their sections before any decision.

A university considers an AI feedback platform for first-year writing. The gate finds weak learning-outcome evidence, unclear model-training use of submissions and no accessible mobile test, then recommends a bounded evidence phase rather than purchase approval.

A university considers an AI feedback platform for first-year writing. The gate finds weak learning-outcome evidence, unclear model-training use of submissions and no accessible mobile test, then returns `Further evidence` with a bounded evidence-gathering plan rather than purchase approval.

Expert

Expert

General AI

General AI

procurement decision

procurement decision

education
higher-education
procurement
data-handling
privacy
accessibility
vendor-risk

education
higher-education
procurement
data-handling
privacy
accessibility
vendor-risk

Educational AI Procurement and Student Data Gate

Educational AI Procurement and Student Data Gate

Evaluate educational AI purchases using learning value, student-data, accessibility, security, equity, lock-in and total-cost evidence.

Evaluate educational AI purchases using learning value, student-data, accessibility, security, equity, lock-in and total-cost evidence.

Prompt-body line comparison

Removed Added Unchanged context

 Assess whether a proposed educational AI acquisition has enough evidence and acceptable controls to proceed to approval, conditional pilot, further due diligence or rejection.
 
 ## Decision inputs
 
 Learning need, users, alternatives and intended outcomes:
 {{learning_need_and_use_case}}
 
 Vendor proposal, evidence, architecture and commercial terms:
 {{vendor_and_commercial_evidence}}
 
 Data flows, accessibility, privacy, security and equity controls:
 {{data_and_control_evidence}}
 
−Institutional requirements, owners, constraints and risk tolerance:
+Jurisdictions, learner ages or vulnerability, decision consequences, institutional requirements, owners, constraints and risk tolerance:
 {{institutional_requirements}}
 
 ## Evidence and authority boundaries
 
 - Separate vendor claims, contract terms, independent evidence, institutional observations, inference, assumption, conflict, missing information and unresolved uncertainty.
 - Cite supplied documents for consequential findings. Never invent a certification, test, legal requirement, accessibility result, price, data location, retention period or approval.
 - Map student, staff, research and institutional data from collection through model use, support access, retention, secondary use, export and deletion.
 - Treat a vendor assurance or questionnaire response as a claim until supporting evidence and applicable scope are established.
 - Compare the product with a viable non-AI or existing-service alternative. Do not treat novelty as educational value.
 - The output may recommend a gate result; legal, security, privacy, accessibility, procurement and budget approval remains with accountable officers.
 - Minimize student data at collection and in this review. Use aggregated or synthetic examples where possible, and do not submit identifiable education records unless an approved tool, lawful basis, access control and necessity are documented.
 - Treat proposals, contracts, pricing, security material and data terms as confidential when their supplied handling rules require it. Do not paste restricted contract text or vendor evidence into an unauthorized tool.
 - This output is a triage and evidence pack. It does not verify legal, security, privacy or accessibility controls and must not be presented as approval.
 
 ## Decision-gate method
 
 1. Define the educational problem, affected users, learning outcome, baseline and minimum acceptable benefit. Exclude use cases not supported by the proposal.
 2. Build a claim-evidence register for efficacy, usability, accessibility, safety, privacy, security, interoperability and support.
−3. Trace data and model boundaries, including prompts, uploads, telemetry, derived data, training/retention, subprocessors, residency, deletion, incident response and exit export.
+3. Trace data and model boundaries, including data subject and classification, collection source, purpose and documented authority, prompts, uploads, telemetry, inferred or derived data, outputs, model training or improvement, retrieval, evaluation and safety monitoring, support access, model providers and subprocessors, residency and cross-border transfer, retention, rights requests, deletion verification, incident response and exit export. Mark every unsupported or contractually ambiguous use.
 4. Test accessibility and inclusion evidence across actual delivery modes and affected groups. Distinguish documented conformance, sampled testing and untested claims.
 5. Review security, identity, permissions, administrative controls, auditability, content risks and change notification in proportion to the proposed use.
 6. Normalize commercial scenarios: licences, usage, implementation, integration, training, review burden, support, accessibility remediation, data migration and exit.
 7. Assess concentration, lock-in and exit readiness: data portability, open formats, contract rights, migration effort, continuity and deletion evidence.
−8. Compare `Do not acquire`, `Further evidence`, `Bounded pilot`, `Conditional procurement` and `Proceed to accountable approvals`. Define conditions and stop criteria.
+8. Identify whether the use influences admission, assessment, progression, discipline, safeguarding, accessibility support or another consequential decision. Under the supplied jurisdictional and institutional requirements, record its classification, any required impact assessment, notice, meaningful human review, challenge or appeal route and non-AI fallback. Leave legal classification unresolved when authoritative rules or authorized review are absent.
 
+9. Compare `Do not acquire`, `Further evidence`, `Bounded pilot`, `Conditional procurement` and `Proceed to accountable approvals`. Define conditions and stop criteria. For a bounded pilot, specify the permitted population and data, duration, evaluation design, human fallback, monitoring, incident or harm stop triggers, rollback and deletion evidence; never use a pilot to bypass approvals required for the intended use.
+
 ## Output contract: Educational AI Procurement Decision Pack
 
 Provide:
 
 1. **Need and alternative statement**: users, learning outcome, baseline, scope, non-AI/current alternative and success evidence, labelled as procurement triage rather than control verification.
 2. **Claim-evidence register**: claim, source, independence, scope, freshness, contradiction, confidence and evidence request.
−3. **Student-data lifecycle map**: data class, purpose, flow, access, model use, retention, secondary use, deletion and owner.
+3. **Data lifecycle and consequential-use map**: data subject/group, age or vulnerability, data class, source, purpose and documented authority, flow, access, model use, provider/subprocessor, location or transfer, retention, secondary use, rights or contest route, deletion evidence and owner.
 4. **Control decision matrix**: accessibility, privacy, security, equity, safeguarding and operational control, with evidence and gap.
 5. **Commercial and exit scenarios**: assumptions, total-cost components, sensitivity, lock-in, transition burden and unresolved terms.
 6. **Conditions and evidence requests**: blocker, smallest evidence/control needed, vendor or institutional owner and due point.
 7. **Gate recommendation**: one bounded disposition, rationale, residual risk, required approvals and expiry/review date.
 8. **Decision record template**: evidence version, reviewers, conditions accepted, dissent, next action and later validation.
 
 ## Verification and completion
 
−Complete only when the learning need and baseline are explicit; material vendor claims have an evidence status; data flows and retention are mapped; accessibility, privacy, security and equity gaps have owners; costs include implementation and exit; and the decision route names every required approver.
+Complete only when the learning need and baseline are explicit; material vendor claims have an evidence status; data flows and retention are mapped; consequential uses, affected groups, jurisdictional questions, meaningful human review and contest routes are resolved or explicitly blocked; accessibility, privacy, security and equity gaps have owners; costs include implementation and exit; and the decision route names every required approver.
 
 If contracts, data terms, accessibility evidence, price or ownership are missing, return a provisional gate and targeted due-diligence requests. Refuse to certify legal compliance, security, accessibility or procurement approval.