Evaluate an AI tool by starting with a specific learning problem, checking instructional fit and student risks, and testing it in a supervised pilot with a clear measure of success. Do not approve it because it is new, popular, or promises personalization: decide in advance who will use it, when, for how long, and what evidence would justify continuing.
1. Define the learning problem before choosing a tool
Write down the instructional task or barrier the AI tool is meant to address. Be specific: for example, identify whether you need to help students practise a target skill, make feedback more useful, adapt materials, or reduce a defined administrative burden. These are possible uses, not proof that a particular product will deliver them.
The U.S. Department of Education’s August 20, 2026 classroom technology guidance proposes five questions to frame an evaluation:
- What learning problem does it solve?
- When should it be used?
- For whom should it be used?
- For how long should it be used?
- What evidence demonstrates that it improves student learning?
Use those answers to describe the intended users, where the tool fits in a lesson, the length of the pilot, and the result that would count as success. If the proposed benefit cannot be connected to a learning need, pause before testing the product.
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2. Check that it fits teaching and learning
Ask whether the tool supports the teacher’s instructional plan and the target skill, rather than displacing an activity for no clear reason. The Department’s October 2024 AI toolkit identifies potential uses such as adapting content, supporting learners with disabilities, helping educators prepare lesson materials, improving feedback, and reducing administrative work. Each remains a hypothesis to test for the product and students in front of you.
Consider what the teacher and students will do differently with the tool, and whether it adds value compared with the current approach. Keep the teacher’s role, the lesson objective, and the intended student benefit visible throughout the evaluation.
3. Review student data practices and terms first
Before students enter information, read the vendor’s current privacy policy and terms of service and use your school or district’s established review process. Find out what information the service collects, who controls it, what the vendor may do with it, how long it is retained, whether it is shared with third parties, and whether prompts or outputs may be used for model training or other purposes. Confirm that the institution has authority to use the service and that the proposed use follows its approval process.
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The U.S. Department of Education’s student privacy resource points schools to a Model Terms of Service checklist for examining collection, use, and transmission of student information. Pennsylvania Department of Education guidance recommends limiting data to what the service needs and checking applicable privacy requirements, including FERPA and COPPA where they apply. Which rules apply depends on the institution, users, data, and deployment; this is not a substitute for local privacy or legal review.
4. Check accessibility, equity, and foreseeable harms
Test whether the intended students can use the interface and understand its outputs. Include students with disabilities and multilingual learners in the evaluation, and consider whether the tool could create unequal barriers or outcomes for a particular group.
Also consider whether use could expose sensitive information, produce unsuitable material, or automate a decision that requires educator judgment. The federal AI toolkit addresses privacy, security, civil rights, accessibility, digital equity, and risk. UNESCO’s 2023 global guidance, on a webpage updated January 16, 2026, recommends human-centered, age-appropriate use of generative AI, with data privacy protection and ethical validation. These concerns belong in the initial evaluation, not only after classroom use begins.
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5. Test outputs and keep a person accountable
Before classroom use, try realistic tasks or prompts aligned with the curriculum. Review the responses for accuracy, relevance, age appropriateness, and possible bias. Treat generated material as fallible; explain its limitations to students and ensure an educator checks outputs before they affect learning or decisions.
Take particular care when AI may influence grading, student placement, or discipline. Pennsylvania guidance says, “Human judgement should always be the final step in the decision-making process.” A tool can inform an educator, but a consequential decision should not be left to an unreviewed AI output.
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6. Run a pilot that measures the intended result
Set a baseline before the pilot and choose a small number of measures that match the problem. Depending on the goal, these might include performance on the target skill, quality of student work, usefulness of feedback, access, engagement, or teacher workload. Set a defined pilot period, then compare results with the baseline and examine which students benefited and which encountered barriers.
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Usage counts, screen time, engagement, and vendor claims by themselves do not show that learning improved. The Department’s guidance calls for evidence about which tools work, for whom, under what conditions, and with what implementation support. Use the results to decide whether to continue, change the implementation, or stop; do not generalize from one classroom or pilot to students or settings that were not evaluated.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Communicate, train, and revisit the decision
Tell students and families when the tool is used, why it is being used, what it can and cannot do, what data it handles, and what alternatives or opt-out arrangements are available under school policy. The Department’s AI toolkit emphasizes transparency, community involvement, and opportunities for students, teachers, and parents to opt out of AI-enabled applications.
Give educators time to learn how to use the tool, review its outputs, and spot errors. Document the purpose, review, pilot measures, and decision. Reassess when the product, its terms, the student population, or the instructional purpose changes. Pennsylvania’s checklist is state guidance, not a universal rule; schools should follow their own applicable requirements and processes.
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How to compare two or more AI tools
Compare products using the same learning problem and, as far as practical, equivalent pilot conditions. Set the importance of each criterion locally: the federal and state guidance does not prescribe universal weights or a single scoring scale.
| Criterion | What to examine |
|---|---|
| Learning impact | Evidence for the intended outcome, including which students benefited and under what conditions. |
| Instructional fit | Whether the tool supports the teacher’s plan and the target skill. |
| Privacy and control | Data collected, permitted uses, retention, sharing, vendor obligations, and whether prompts or outputs may be used for training. |
| Access and equity | Usability for students with disabilities and multilingual learners, and the possibility of unequal effects. |
| Output quality and oversight | Accuracy, relevance, suitability, bias, and whether an educator can review and remain accountable for use. |
| Implementation | Educator training, communication, time required, and the school’s capacity to monitor and revise use. |
Use the right review process for your school
This approach draws on U.S. federal and Pennsylvania guidance alongside UNESCO’s global guidance; it is not jurisdiction-specific legal advice. Apply your institution’s privacy, accessibility, procurement, and legal review processes before deployment. Requirements vary with the school, students, data, and intended use. The sources call for evaluating evidence; they do not establish that AI tools as a class improve learning.
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