Build a maths practice generator
Provide teachers and learners with a mobile interface for generating practice sets from a checked question bank.
How this works
Provide teachers and learners with a mobile interface for generating practice sets from a checked question bank. Use this to assemble practice sets from educator-checked material rather than generate unchecked exam questions on demand. A mobile interface should make topic and difficulty choices clear and preserve the answer key's relationship to each question.
Build a narrow prototype around a versioned question bank, selection rules and a deterministic set manifest. AI may classify or explain approved questions, but an educator validates new items and solutions before they enter the usable bank. This is practice support, not high-stakes grading.
Before you start
Checked question bank
Provide question IDs, topic, difficulty, wording, answer and worked solution.
Selection rules
Define allowed topics, set length and repetition constraints.
Prototype environment
Use an isolated local or staging app and available development tools.
Prepare the inputs
- question_bank
- Required
versioned structured items
Each usable question must have an educator-approved answer key.
- practice_request
- Required
topic, level, count and optional seed
Reject unsupported topics and excessive set sizes clearly.
- presentation_rules
- Required
notation, units and accessibility requirements
Preserve mathematical symbols across mobile layouts.
- validation_cases
- Required
known sets and boundary requests
Include empty topics, insufficient items and duplicate prevention.
Implementation, step by step
Audit the bank
- Check unique IDs, missing solutions and topic coverage.
- Quarantine unreviewed or contradictory items rather than silently repairing their mathematics.
Checkpoint: A usable-item manifest is available.
Define set assembly
- Filter by requested topic and level and sample without repetition according to the rules.
- Record the bank version and selection seed when reproducibility is useful.
Checkpoint: The same saved request can reproduce its set.
Build the learner interface
- Implement a small mobile-friendly form and readable question view.
- Show unsupported requests and loading failures without losing the selected options.
Checkpoint: The prototype covers normal and empty states.
Attach checked feedback
- Connect each question to its correct answer and worked solution.
- If AI rephrases an explanation, compare it with the approved solution before release.
Checkpoint: Question and answer IDs remain aligned.
Validate the prototype
- Run known requests and inspect small-screen notation, keyboard use and answer visibility.
- Have an educator review sample sets before learner use; preserve test results and remaining gaps.
Checkpoint: The prototype is ready for a bounded practice pilot.
Decisions along the way
Too few approved items
Offer a smaller set or broader topic rather than inventing unreviewed questions.
Question permits several valid answers
Encode the accepted forms and have an educator verify them.
Learner requests an exam score
Keep grading outside scope unless a separately validated rubric is supplied.
What you will produce
practice-set.json
JSONA reproducible practice set with checked solutions.
Include: set_id · bank_version · request · question_ids · answer_key
prototype-review.md
MarkdownA review record for educators and developers.
Include: tested_flows · math_checks · device_checks · open_issues
Check the result
- Verify every displayed question maps to the correct answer key.
- Solve sampled items independently of generated explanations.
- Test insufficient-bank and unsupported-topic requests.
- Inspect notation and controls on representative mobile widths.
When something goes wrong
Formula rendering breaks
Use a readable fallback and hold affected items from learner use.
Bank update changes an answer
Version existing sets and require educator review of the changed item.
Build or test fails
Keep the bounded patch and error evidence; do not call the prototype ready.
A worked example
SYNTHETIC EXAMPLE · NOT A REPORTED BUSINESS RESULT
Request
Synthetic: create a short linear-equation practice set.
Inputs
Bank Q1: x+3=7, answer 4; Q2: 2x=10, answer 5; Q3 is unreviewed. Request asks for three approved questions.
Expected result
Synthetic result: report only two approved items available and offer a two-question set. The manifest contains Q1 and Q2 with answers 4 and 5; Q3 is excluded.
Run a small pilot
Start small
Use one topic and a small educator-reviewed bank with a few learners.
Record the baseline
Record manual set-preparation time and question or interface errors.
Agree success criteria
Agree mathematical correctness and usable-flow criteria before adding topics.
Stop or revise
Stop learner access to any item with an unresolved answer-key discrepancy.
Keep the scope clear
- Do not treat generated explanations as educator validation.
- Keep learner data minimal and prototype changes within the agreed feature scope.
Check before using
Have educators validate questions and solutions.
Business activities
- Build and change software
- Deliver courses and training
FROM A RECIPE TO YOUR WORKFLOW
Bring the company context together.
This workflow needs reliable inputs before it needs a better prompt. Moat8 Vault brings working information into a readable archive for the AI tools you use.
For this recipe: A checked question bank with worked solutions · Topic, difficulty, and exam-length requirements
Start with one recurring task, agree which sources to use, and review the first result with your team.
Discuss this workflow