EdTech AI Procurement Checklist: What Universities Must Ask Vendors

Why AI procurement is different for EdTech

AI-enabled EdTech review needs to look beyond the product demonstration. Vendor demos often show the user experience, but procurement review needs the control plane. Universities should understand how the product operates in the institution’s environment, including any AI agents, connector frameworks, plugins, APIs, and Model Context Protocol or similar connector layers.

The review should test whether the vendor can protect student and institutional data, document AI risk, enforce university policy at runtime, constrain AI agent permissions, log meaningful activity, and support operational changes after purchase.

How to evaluate vendor readiness before approval

Procurement teams should ask for evidence, not assertions. The strongest review package is specific enough for security, privacy, legal, IT, academic technology, and operational stakeholders to understand what the system does and where controls are enforced.

Start with the AI system architecture, not the demo

Universities should request a current architecture package that explains how the product operates in the institution’s environment, including any AI agents or connector frameworks.

1. Data-flow diagram

   Require a diagram showing institutional data sources, model or provider endpoints, subprocessors, logging stores, administrative consoles, tool connectors, and every campus system touched by the product.

2. Role and permission matrix

   Ask for permissions covering students, faculty, staff, administrators, support personnel, service accounts, AI agents, plugins, external connectors, and vendor personnel with support access.

3. Runtime enforcement points

   Confirm where policy is enforced for prompts, responses, retrieval, file uploads, administrative overrides, tool calls, exceptions, and human approval steps.

4. Connector and MCP review

   Treat Model Context Protocol or similar connector layers as security-relevant integration infrastructure. Review servers, tools, resources, authentication, authorization, and logging.

5. Audit architecture

   Require tamper-resistant logs for authentication events, administrative actions, policy changes, data access, prompt activity where retained, tool calls, connector failures, and investigations.

The core EdTech AI procurement checklist

Use the checklist as a structured way to separate vendor claims from reviewable evidence. Each item should be answered with current documentation, contractual terms, or a sandbox demonstration.

• Can the vendor show how student and institutional data is accessed, stored, inferred, logged, shared, retained, or deleted?
• Can the vendor provide data-flow diagrams that include model or provider endpoints, subprocessors, logging stores, administrative consoles, tool connectors, and campus systems?
• Can the vendor provide a role and permission matrix for users, administrators, service accounts, AI agents, plugins, external connectors, and vendor support access?
• Can the vendor show where university policy is enforced at runtime for prompts, responses, retrieval, file uploads, administrative overrides, tool calls, exceptions, and human approval steps?
• Can the vendor demonstrate least-privilege access and tool-call controls in a sandbox before production use?
• Can the vendor provide retention schedules, subprocessor lists, HECVAT or equivalent security responses, secure development evidence, and incident-response terms?
• Can the vendor provide meaningful, tamper-resistant logs for authentication events, administrative actions, policy changes, data access, prompt activity where retained, tool calls, connector failures, and investigations?

Contract and operating questions universities should not skip

Approval should account for what happens after purchase. Universities should understand how the vendor supports operational changes, policy updates, incident response, changes to subprocessors, changes to retention, and changes to connectors or AI agent permissions.

Procurement, legal, IT, security, privacy, and academic technology teams should be able to review the same evidence package and reach a shared understanding of what the product can do, who or what can access institutional systems, and which logs will be available for investigations.

Where runtime governance fits in the buying decision

Runtime governance matters because many AI risks appear during use, not only during configuration. Universities should require proof of policy enforcement, monitoring, audit logging, tool-call controls, connector controls, and incident-response readiness before production deployment.

Trussed AI helps enterprise teams think through runtime governance, agent permissions, policy enforcement, MCP security, and auditability for AI agent deployments.