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A district should not begin with an impressive demo or a list of features. Begin with a defined instructional problem, then test whether the AI tool improves that outcome for the intended learners without surrendering privacy, educator judgment, accessibility, or accountability. The U.S. Department of Education’s August 20, 2026 guidance gives a useful starting test: What learning problem does it solve?
Ask four companion questions: when should it be used, for whom, for how long, and what evidence demonstrates improved student learning?
What a district must establish before approval
Adoption is justified only when the district can connect a specific learning need to a defensible use of the tool, acceptable information practices, meaningful human control, and measurable results. A polished interface, high student engagement, or a vendor’s description of its own product does not establish learning impact.
This is general U.S.-oriented procurement guidance, not legal advice or a finding that any particular product complies with law. State and local requirements, contract language, and the tool’s actual data practices must be checked by the district. Vendor terms, features, and supporting studies can change, so verify them during procurement.
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Write answers before scheduling a pilot or negotiating a contract. The Department’s August 20, 2026 education-technology guidance uses these questions to keep technology subordinate to instruction.
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- What learning problem does it solve? Name the skill, misconception, access barrier, teacher workload, or other educational need. “Use AI in mathematics” is not a use case; “give ninth-grade algebra students immediate hints on solving linear equations while the teacher works with a small group” is.
- When should it be used? Specify the lesson phase, assignment type, setting, and conditions. Decide whether it is appropriate for drafting, practice, feedback, assessment, intervention, or teacher planning.
- For whom should it be used? Identify grades, courses, language profiles, disability-related needs, and the staff who will operate or supervise it. A tool suitable for adult lesson planning may be unsuitable for unsupervised elementary-student tutoring.
- For how long should it be used? Set a duration and boundaries. Continuous use can displace productive struggle, discussion, reading, or teacher interaction; a short, targeted use may be more appropriate.
- What evidence demonstrates that it improves student learning? Define the outcome before looking at a dashboard. Evidence should match the district’s students, use case, comparison or baseline, and time frame.
Record the answers in a one-page use-case statement. Require the vendor and internal sponsor to respond to that statement rather than allowing a feature tour to redefine the problem.
Build an evaluation team with authority to say no
AI procurement affects instruction, privacy, accessibility, labor, safety, and public trust. Include the people who can evaluate each area and who will be accountable after launch.
- An instructional leader and classroom educators who understand the target lessons.
- Teachers or specialists familiar with the intended students, including multilingual learners and students with disabilities.
- Information-technology and security staff who can inspect integrations, accounts, access controls, logging, and availability.
- Privacy, records, procurement, and legal reviewers who can examine the policy and contract.
- Students and families, or representatives who can explain practical effects and concerns.
- Accessibility expertise capable of testing the complete user experience, not merely reading a conformance statement.
Give the team a written decision rule, a deadline, and access to the product’s documentation. A committee that can only advise, while another office has already promised adoption, is not an effective safeguard.
Demand evidence that matches the claimed outcome
Separate engagement from learning
Usage counts, time in the application, completion rates, and positive surveys can indicate participation, but they do not prove that students learned more or retained the skill. Ask what was measured, with which students, under what instructional conditions, and against what baseline or comparison.
Test independence and relevance
Request the underlying study, not only a success story. Identify the author, funding, sample, grade and subject, intervention length, outcome measure, comparison group, and limitations. A vendor-run demonstration with a different population may help explain the product but should not be treated as independent evidence. The Department’s 2026 guidance calls for independent evidence and regular effectiveness reviews.
Define success before the pilot
Choose a small set of observable outcomes, such as performance on a common task, quality of explanations, completion of a targeted intervention, or teacher time returned to instruction. Establish a baseline, collection method, review dates, and a threshold that would trigger continuation, modification, or discontinuation. Do not change the success definition after seeing disappointing results.
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Review privacy and information handling before student use
Do not let a classroom trial precede the privacy review. Read the privacy policy, terms of service, security documentation, data-retention language, and any education-specific agreement. Map every category of information that could enter the service, including names, identifiers, free-text responses, audio, images, disability information, grades, behavioral notes, teacher prompts, and generated outputs.
Questions for the data map
- What information is collected directly, inferred, or generated?
- Is data used to provide the service only, or also for analytics, advertising, model training, or product development?
- Who receives or can access it, including subcontractors and model providers?
- Where is it stored and transmitted, and for how long?
- Can the district delete records, retrieve them, correct them, and verify deletion?
- What happens when an account is closed, a student transfers, or a contract ends?
- Which controls are available to limit collection, restrict roles, review logs, and disable optional features?
- What incident-notification, security, and business-continuity commitments are contractual?
The Department of Education’s Privacy and Education Technology resources include guidance on online educational services and a Model Terms of Service checklist. Use that checklist to organize questions, but do not treat it as a substitute for district counsel or as a product-specific legal determination. Confirm how the contract addresses applicable federal, state, and local obligations rather than assuming a marketing statement settles compliance.
Minimize what enters the tool
Design the use case so the tool receives the least information needed. Prefer pseudonymous student identifiers, avoid sensitive narrative details when a de-identified example will work, restrict staff permissions, and prohibit copying unrelated records into prompts. Document retention and deletion settings and test them before deployment.
Keep educators in control and make outputs understandable
The Department’s 2023 report, Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations, says AI systems and tools should be “inspectable, explainable, and provide human alternatives to AI-based suggestions.” Translate that principle into observable product requirements.
Inspectability
Review what input the system used, what output it produced, which version or configuration was active, and what logs are available to an authorized educator. If a recommendation cannot be examined after it affects a student, the district cannot investigate an error.
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Ask the vendor to describe how the system generates feedback, classifications, recommendations, or scores in language a teacher can use. Identify known failure modes, confidence limitations, unsupported subjects or languages, and circumstances in which the output should be ignored. Require prominent notices when content may be inaccurate or incomplete.
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Override and human alternatives
Teachers must be able to reject, edit, or bypass a suggestion without penalty to the student. Determine whether a student can receive equivalent human support when the system is wrong, inaccessible, unavailable, or inappropriate. Establish who makes the final instructional or disciplinary decision; the software should not silently become the decision-maker.
Accountability for consequential uses
Do not use an opaque output as the sole basis for grades, placement, discipline, special-education decisions, or access to services. If the district permits a decision-support use, require documented human review, an appeal path, and a record of the reasoning that led to the final decision.
Check accessibility, equity, and age-appropriate use
Evaluate the experience with the actual learners and devices, not only with a procurement team. Test keyboard navigation, screen-reader behavior, captions and transcripts, contrast, text resizing, timing, language support, and compatibility with assistive technology. Check whether a student can complete the task when connectivity is weak or a device is shared.
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Compare output quality across relevant groups and contexts. A tool may work well for standard written English but provide weaker feedback for multilingual learners, dialect variation, or communication devices. Examine whether prompts, examples, or recommendations encode assumptions that disadvantage a group. Pennsylvania Department of Education guidance identifies accessibility and meaningful instructional support as evaluation considerations.
Set age-appropriate rules for accounts, collection, monitoring, and independent use. UNESCO’s human-centered guidance highlights data privacy, age-appropriate use, and ethical validation. The Department’s July 2025 AI guidance also emphasizes engaging affected stakeholders, especially parents, in adoption and deployment decisions.
Make equity testable
- Define which learner groups must be included in pilot data.
- Compare the agreed learning outcomes and error patterns across those groups.
- Provide a non-AI path when a learner cannot use the tool or when its output is unsuitable.
- Budget for devices, connectivity, training, translation, and specialist support so access is not determined by a family’s resources.
- Publish a plain-language explanation of the tool’s role, limits, and complaint route.
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When comparing products, hold the use case, student group, duration, and outcome measures constant. The sources do not establish a universal scoring rubric or weighting; the district should set weights according to the learning need and local requirements. Record evidence, unresolved questions, and conditions of approval rather than assigning an unexplained single score.
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| Criterion | Evidence to request | Decision question |
|---|---|---|
| Instructional fit | Use-case mapping, curriculum alignment, examples from the intended grade and subject | Does it address the stated learning problem without displacing a more effective practice? |
| Learning evidence | Independent studies, methods, outcomes, limitations, and population match | Is there credible evidence for the outcome this district will measure? |
| Privacy and data | Privacy policy, terms, data-flow map, retention and deletion controls, contract commitments | Can the district limit collection and control the information throughout its life cycle? |
| Human control | Review screens, explanations, override functions, human fallback, audit logs | Can educators understand, challenge, and replace an output? |
| Accessibility and equity | Hands-on accessibility testing, language coverage, subgroup results, alternative access | Can every intended learner use it and benefit fairly? |
| Implementation | Training plan, support hours, integration requirements, family communication materials | Can the district operate it reliably with available staff and resources? |
| Monitoring and exit | Outcome dashboard, review calendar, export and deletion process, termination terms | Can the district detect failure and stop without losing records or instructional continuity? |
Run a controlled pilot, not an open-ended rollout
- Write the pilot charter. State the instructional problem, participating classes, permitted activities, prohibited uses, duration, measures, privacy controls, and decision date.
- Train educators first. Cover the tool’s workflow, limitations, privacy rules, accessibility accommodations, error reporting, and the human alternative. Training should include practice with incorrect or unsafe outputs.
- Tell families and students what will happen. Explain when the tool is used, what information enters it, how humans review outputs, how to request an alternative, and where concerns go.
- Collect baseline information. Use the same or comparable task before the intervention. Record relevant context such as instructional time, attendance, device access, and teacher support so results are interpretable.
- Monitor during use. Review output errors, subgroup experience, outages, privacy incidents, teacher workload, and student reliance. Provide a fast route to suspend a feature or class if harm appears.
- Review against the pre-set thresholds. Combine learning evidence with privacy, accessibility, implementation, and cost-to-operate findings. A learning gain does not excuse unacceptable data practices, and a popular tool does not pass if the agreed outcome is unchanged.
The Department’s 2026 guidance calls for implementation support, educator training, communication about use and outcomes, and regular effectiveness review. Treat the pilot as a decision process with a defined end, not as an entitlement to expand.
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Every agreement should state how the district can pause, narrow, or terminate use. Specify notice periods, data export and deletion, account closure, support during transition, outstanding incident obligations, and access to records needed for student services or public accountability. Keep a non-AI instructional path ready so a technical or procurement decision does not interrupt learning.
Continue only when the evidence supports the intended outcome and the controls work in practice. Modify the use case when the tool is useful only under narrower conditions. Change course when learning is not improving, implementation demands exceed capacity, or equity and privacy requirements cannot be met. The Department’s 2026 guidance says schools should remove a tool when repeated findings show persistent shortcomings.
Common evaluation failures and their remedies
Starting with a feature instead of a need
Failure: A demonstration creates pressure to find a classroom problem for the product. Remedy: Approve a written use-case statement and five-question review before demonstrations influence procurement.
Treating vendor claims as proof
Failure: A case study or engagement metric is presented as learning evidence. Remedy: Request methods, independent confirmation, population match, and a district measurement plan.
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Signing before mapping data
Failure: Students enter personal information during a “temporary” trial. Remedy: Complete policy, terms, data-flow, retention, deletion, and contract review before accounts are created.
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Making the algorithm the final authority
Failure: Teachers cannot inspect or override a recommendation. Remedy: Require explainability, auditability, human alternatives, and an appeal process as approval conditions.
Ignoring learners who are hardest to serve
Failure: A tool is tested only with fluent readers on current devices. Remedy: Include accessibility specialists and representative learners, compare subgroup results, and fund alternatives.
Launching without a stop rule
Failure: The district continues because retraining or cancellation feels difficult. Remedy: Set review dates, thresholds, termination rights, data-exit steps, and an owner for the decision before deployment.
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Keep a concise file that another administrator, teacher, family, or auditor can understand. It should contain the use-case statement; evidence review and limitations; privacy and security findings; accessibility and equity results; contract conditions; training and communication materials; pilot baseline and outcomes; incident and exception logs; the review schedule; and the written decision to continue, modify, pause, or stop.
This record turns AI adoption from a technology purchase into an accountable instructional decision. It also makes the next review faster when the vendor changes a model, terms, interface, or data practice.
Quick Recap
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