Can AI Generate Practice Questions for a Tutor? A Human Review Checklist

Short answer: Yes. A tutor can use AI to draft practice questions, answer keys, distractors, hints, and explanations, but the output should be treated as an editable draft—not as a validated assessment. The tutor remains responsible for checking whether every item is answerable, accurate, appropriately difficult, aligned to the source material, and suitable for the learner.

This workflow is especially useful when you have a lesson, chapter, worked example, or learning objective and want several candidate questions quickly. It is not a substitute for subject expertise, approved assessment procedures, or professional judgment. Current education guidance emphasizes AI that supports educators and learners while keeping people involved in decisions and paying attention to privacy and stakeholder engagement [1].

What AI is good at—and what it is not

Generative AI can produce variations quickly. Given a clearly bounded source, it can suggest recall questions, application problems, multiple-choice options, hints, and short explanations. That makes it useful for brainstorming and first drafts. It can also reformat an item for a different age group or produce a sequence that moves from a simple example to a more demanding one.

However, fluent wording is not evidence of correctness. A model may invent a fact, misread a diagram, use a method that the learner has not been taught, write two plausible answers, or make a question impossible to answer from the supplied material. The Institute of Education Sciences reports that rigorous causal evidence about current AI tools and student outcomes remains limited, and that outcomes depend substantially on how tools are designed and integrated into instruction [2]. For a tutor, the practical implication is simple: use AI to expand options, then use human review to decide what a learner actually sees.

A five-stage workflow for AI-drafted practice items

1. Define the instructional target

Start with the objective, not with a request such as “make me a quiz.” Write down the topic, learner level, prerequisite knowledge, permitted methods, item type, number of questions, and what a correct response must demonstrate. If you are working from a text, provide only the relevant excerpt or a concise teacher-created summary. State whether the item should test recall, interpretation, application, comparison, or explanation.

A useful prompt has four parts: source boundary, learning target, constraints, and draft format. For example: “Using only the supplied passage, draft three eighth-grade questions that test identifying evidence. Include one correct answer, three plausible distractors, a one-sentence rationale, and a note identifying the sentence in the passage that supports the answer. Mark anything uncertain for review.” The request should ask for a draft and should not imply that the model can certify quality.

2. Generate more candidates than you need

Ask for a small batch of candidates rather than publishing the first response. More options make it easier to discard repetitive, ambiguous, or weak items. Keep the source, prompt, model or tool, date, and output together in a working record so you can tell which version was reviewed. Do not paste a learner’s name, contact details, individualized record, free-form counseling conversation, or other unnecessary personal information into a general-purpose service.

If the material concerns a child, privacy questions can become especially important. The Federal Trade Commission says the Children’s Online Privacy Protection Rule applies to operators of services directed to children under 13, and to certain services with actual knowledge that they collect personal information from children [3]. The exact obligations depend on the service, audience, and data practices, so consult current primary rules and qualified professionals for a particular program. As a baseline workflow, minimize data, use de-identified examples, check the service’s current terms and settings, and follow your school, organization, or platform requirements.

3. Check the answer before checking the style

First solve every item yourself or verify it against the supplied source. For a calculation, recompute the result by an independent method. For a reading question, point to the exact supporting passage. For science or history, check dates, definitions, units, causal language, and the scope of the claim. If you cannot explain why the answer is correct, the item is not ready.

Then inspect answerability. A learner should have enough information to determine an answer without guessing what the tutor intended. Remove hidden assumptions, undefined terms, missing diagrams, contradictory instructions, and prompts that depend on a fact outside the stated material. Watch for words such as “always,” “best,” or “most important,” which can create multiple defensible interpretations unless the criterion is explicit.

4. Review difficulty, distractors, and explanations

Difficulty is not the same as obscurity. A challenging item can require a meaningful step of reasoning; a poor item may simply use unfamiliar vocabulary or omit context. Ask what makes the item hard and whether that difficulty matches the lesson goal. If the target is proportional reasoning, do not accidentally turn the question into a test of advanced reading comprehension.

For multiple-choice items, each distractor should represent a recognizable misconception or a plausible error. Avoid jokes, grammatical clues, unequal answer length, “all of the above” shortcuts, and options that are obviously unrelated. Check that only one option is best under the stated question. If two options could be defended, revise the stem or change the item type.

Explanations need their own audit. A useful explanation identifies the reasoning step, not merely the answer. It should not introduce a new claim that was absent from the lesson without labeling it as extension material. For a tutoring session, consider separating a first hint from a full explanation so the learner has an opportunity to attempt the problem. Education research summarized by IES describes more promising uses as those that support teacher mediation and student thinking rather than replacing the learner’s cognitive work [2].

5. Pilot, observe, and revise

Before using a batch widely, try it with the intended lesson context and a small, appropriate review process. Ask: Did the question elicit the target skill? Did the wording confuse the learner? Did the hint reveal too much? Did the explanation correct the misconception? Record the revision, not just the final wording. A tutor can also read the item aloud, because awkward syntax and missing context are easier to notice in spoken form.

Do not use AI-drafted items as the sole basis for high-stakes decisions or as proof that a learner has mastered a standard. The U.S. Department of Education’s current AI guidance discusses AI-enhanced high-impact tutoring and high-quality instructional materials while emphasizing responsible integration and engagement of affected stakeholders [1]. Local rules, school procedures, accommodations, and approved assessment practices may impose additional requirements.

Human review checklist

Use this checklist for every item. “Pass” means you can show your reasoning or evidence; “revise” means the item stays out of the learner-facing set.

• Alignment: Does the item test the stated objective rather than an accidental side skill?
• Source fidelity: Can the answer be supported by the supplied material or a clearly identified prerequisite?
• Answerability: Is all necessary information present, with no hidden assumption or missing visual?
• Correctness: Have the answer, units, definitions, and reasoning been independently checked?
• Uniqueness: Is there one clearly best answer, or is the response rubric precise enough for an open-ended item?
• Difficulty: Does the cognitive demand fit the learner and the lesson sequence?
• Distractors: Are incorrect options plausible, distinct, and tied to likely mistakes?
• Explanation: Does the rationale show the reasoning without doing all the thinking for the learner?
• Accessibility: Is the language, layout, notation, and required context appropriate for the learner’s needs?
• Data minimization: Did the drafting process avoid unnecessary learner information and follow applicable organizational safeguards?
• Use boundary: Is the item clearly labeled as tutor-reviewed practice material, not a certified or high-stakes assessment?

An original go/no-go decision tool

Score each category from 0 to 2: 0 means not demonstrated, 1 means partly demonstrated or needing a minor edit, and 2 means demonstrated with evidence. Rate alignment, correctness, answerability, difficulty fit, and explanation quality. If any of the first three categories scores 0, stop and revise. If the total is below 8 out of 10, keep the item in the draft pool. A score of 8 or higher means “eligible for tutor review,” not automatic approval; the tutor still makes the final decision and records any caveat.

This tool is intentionally conservative. It distinguishes a convenient drafting aid from an assessment instrument. It also makes review visible: another tutor should be able to understand what was checked, what source was used, and why an item was included.

Common failure modes to catch early

Confidently wrong answers: Require a source pointer or independent calculation. Ambiguous stems: Add the comparison criterion, time frame, or expected response. Difficulty drift: Compare the item with an example the learner has already completed. Distractor giveaways: Rewrite options so grammar and length do not reveal the answer. Over-helpful explanations: Split the support into graduated hints. Privacy oversharing: Replace personal context with a fictional or de-identified scenario and consult the current primary rules for the service you use.

Bottom line

AI can reduce the time needed to brainstorm tutoring practice questions, but it does not remove the need for subject knowledge and instructional judgment. The safest practical pattern is bounded source material, explicit objectives, multiple draft candidates, item-level verification, a conservative checklist, and a tutor’s recorded approval. Use the learner’s response as feedback about the lesson and the item—not as evidence that an AI-generated question was inherently valid.

Sources and further reading

1. U.S. Department of Education, “U.S. Department of Education Issues Guidance on Artificial Intelligence Use in Schools”.
2. Institute of Education Sciences, REL Northeast & Islands, “AI in K–12 Education: The Good, the Bad, and the Guardrails to Consider”.
3. Federal Trade Commission, “Children’s Online Privacy Protection Rule (COPPA)”.
4. Federal Trade Commission, “Complying with COPPA: Frequently Asked Questions”.