[Steve Side Hustler]

Hello — I’m a non-technical learner/volunteer looking for simple, practical ways to use AI to create real-world math word problems (for kids and adult learners). I want clear steps I can follow, examples I can copy, and tools that don’t require coding.

What I’m hoping to learn:

• Easy prompt examples to ask an AI to make word problems (with age or grade level specified).
• Simple tools or apps that are beginner-friendly and safe to use.
• How to check the problems for accuracy and appropriate difficulty.
• Ways to make problems feel real-world (shopping, travel, hobbies) without being too complex.

If you can, please share one short prompt you use and one sample problem the AI produced. Practical tips, favorite free tools, or common pitfalls to avoid would be very helpful. Thank you — I’d love to hear what’s worked for you!

[Jeff Bullas]

Thanks for kicking this off — great to see interest in using AI to create real-world math word problems. That curiosity is the best starting point.

Why this works: AI lets you quickly generate context-rich problems tailored to age, curriculum, and real-life scenarios. You get scalable practice items, varied contexts, and step-by-step solutions for students or adults brushing up on skills.

What you’ll need

• A clear goal: grade level or skill (arithmetic, percentages, algebra).
• Example problems or templates to show style and difficulty.
• An AI writer (chat model) you can access via an app or web tool.
• Time to review and adjust outputs — AI helps, you validate.

Quick checklist — do / do not

• Do give the AI precise constraints: topic, numbers range, real-world context, language level.
• Do ask for answers and worked steps so students can learn process, not just results.
• Do not trust outputs without a quick accuracy check (common-sense math check).
• Do not use overly vague prompts — they create fuzzy, unrealistic problems.

Step-by-step: generate your first set

1. Decide grade and topic (e.g., Grade 6 — fractions and percentages).
2. List real-world contexts (shopping, cooking, sports, travel).
3. Draft a clear prompt (see example below).
4. Run AI to generate 10 problems with answers and step-by-step solutions.
5. Quickly verify 2–3 problems for math accuracy and realism; tweak prompt if needed.
6. Adjust tone and difficulty based on learner feedback.

Copy‑paste AI prompt (use as-is)

“Create 10 Grade 6 word problems about shopping and cooking that involve fractions and percentages. For each problem, provide: (1) a concise problem statement in plain language, (2) the full numerical answer, and (3) a step-by-step solution showing work. Use realistic prices and quantities, keep numbers reasonable for mental math, and avoid negative numbers. Label each problem 1–10.”

Worked example (one result you can expect)

Problem 1: Sarah buys 3/4 of a kilogram of sugar and uses 40% of it for baking. How much sugar did she use?
Answer: 0.3 kg.
Steps: Convert 3/4 kg = 0.75 kg. 40% of 0.75 = 0.4 × 0.75 = 0.30 kg.

Common mistakes & fixes

• If problems are unrealistic: increase real-world constraints in prompt (locations, prices).
• If math is wrong: ask AI to show arithmetic steps and check yourself or use a calculator.
• If language is too complex: request simpler wording and shorter sentences.

Action plan (next 30 minutes)

1. Pick a topic and grade.
2. Use the copy-paste prompt above and generate 10 problems.
3. Review 3 for accuracy and realism; tweak prompt and repeat.

Small experiments lead to quick wins — generate, check, adjust, repeat. AI speeds creation; your judgment keeps it useful.

[aaron]

Quick win: Use AI to build 50 realistic, grade-tailored math word problems in under an hour — then validate and deploy a lesson.

The problem: AI can create lots of word problems, but they’re often unrealistic, mathematically sloppy, or mismatched to learner level.

Why this matters: High-quality practice requires accuracy, real-world relevance, and clear steps. Bad problems waste time and teach incorrect methods.

Experience-driven principle: Prompt with constraints, ask for worked steps, verify a sample, then scale. I run 3-minute checks on 10% of output before releasing content.

Do / Do not

• Do specify grade, topic, number ranges, context, language simplicity, and answer format.
• Do request step-by-step solutions and common misconceptions to avoid.
• Do not accept problems without a numeric sanity check and a one-line real-world plausibility note.
• Do not use vague prompts like “make math problems” — they produce garbage.

Step-by-step (what you need, how to do it, what to expect)

1. What you’ll need: target grade, topics (e.g., percentages, fractions), 10–50 minute block, AI chat tool, calculator for spot checks.
2. Draft a clear prompt (example below). Tell AI: number of problems, contexts, numeric ranges, answer + steps, distractors for multiple choice if needed.
3. Run it and generate 10–20 problems first. Expect ~80% good; some errors are normal.
4. Validate: check 3 problems for arithmetic and realism. Fix prompt and regenerate as needed.
5. Scale to 50 problems, split by difficulty and context, export to your lesson format.

Metrics to track

• Generation speed: problems/minute.
• Accuracy: % of problems passing arithmetic check (target >95%).
• Rework rate: % requiring prompt tweaks.
• Student success: % correct on first attempt (if deployed).

Common mistakes & fixes

• Math errors: ask AI to show arithmetic steps and rerun only the faulty items.
• Unrealistic contexts: add locale, prices, or cultural details to prompt.
• Language too complex: request “short sentences, plain language” and “reading level: grade X.”

One-week action plan

1. Day 1: Pick grade/topic and run the copy-paste prompt below for 10 problems.
2. Day 2: Validate 3 problems, tweak prompt for realism/language.
3. Day 3–4: Generate 40 more, grouped by difficulty.
4. Day 5–7: Pilot with 5 learners, collect accuracy and clarity feedback, iterate.

Copy‑paste AI prompt (use as-is)

“Create 10 Grade 6 word problems about shopping and cooking that involve fractions and percentages. For each problem provide: (1) a concise problem statement in plain language, (2) the numeric answer, (3) a step-by-step solution showing each arithmetic step, and (4) one common student mistake and a one-line note on why the problem is realistic. Use realistic prices and quantities, keep numbers within 1–100, and label problems 1–10.”

Worked example you’ll get

Problem 1: Emma buys 0.8 kg of flour and uses 35% of it for bread. How much flour did she use? Answer: 0.28 kg. Steps: 0.8 × 0.35 = 0.28 kg. Common mistake: multiplying 0.8 × 35 (forgot percent conversion). Realistic note: 0.8 kg is a typical home-bakery quantity.

Deliver the prompt, validate 3 items, then scale. Short test now: generate 10 problems and check 3. — Aaron Agius. Your move.

[Fiona Freelance Financier]

Nice: the framework you shared is practical and fast. Keep the process simple so it doesn’t become a stress project — a short routine of create, spot-check, tweak will get you reliable, realistic problems without hours of editing.

What you’ll need

1. Target learners: grade or adult skill level and any reading-level detail.
2. Topics and constraints: e.g., fractions, percentages, one-step algebra, plus number ranges and realistic units (kg, $, km).
3. An AI chat tool you can access and a calculator for fast verification.
4. A simple template for outputs you want (problem, numeric answer, step-by-step work, one-line realism note).

How to run one fast session (what to do)

1. Set a 30–50 minute block. Decide grade/topic and 10–20 contexts (shopping, cooking, travel).
2. Tell the AI the exact output format you want (number of problems, each with answer and steps) and list the constraints (numeric ranges, no negatives, plain language).
3. Generate 10 problems first. Don’t try to perfect everything on pass one — treat it as a draft batch.
4. Spot-check 3 problems for arithmetic and realism. If one fails, note how it failed and update your constraints (e.g., “use prices in $1–50” or “keep fractions simple”).
5. Regenerate only the faulty items or run another 10 once your prompt is tightened.
6. When you have a clean 10, scale to 50 by repeating the tightened prompt and grouping outputs by difficulty or context for lessons.

What to expect and simple metrics

• Expect ~70–90% usable on first pass; aim for >95% accuracy after one quick tweak.
• Track generation speed (problems/minute), accuracy (spot-check pass rate), and rework rate (how many needed prompt tweaks).
• Use a 3-minute sanity check on 10% of the final set before releasing — it prevents most errors.

Common fixes

• If math is wrong: require the AI show every arithmetic step and rerun only the bad items.
• If contexts feel unrealistic: add local examples (common store names, typical pack sizes) and limit price ranges.
• If language is too dense: request “short sentences, plain language, grade X reading level.”

Small, repeatable routines beat big, infrequent efforts. Generate, check three, fix the prompt, then scale — that simple loop will keep your workload light and your problem set reliable.

[aaron]

Agreed: your “generate, check three, fix, scale” loop is the right backbone. Here’s how to add a quality gate and variety system so you get consistent, realistic problems at speed — and know it’s working.

Quick win (5 minutes): Run this two-prompt loop to generate 12 realistic, accurate problems and auto-QA them before you touch a calculator.

1. Generator prompt (copy-paste):“Generate 12 Grade 6 real-world math word problems practicing fractions and percentages. Mix contexts from shopping, cooking, travel, sports. Use numbers 1–100 only, no negatives. Output for each: (a) Problem #, (b) Context, (c) Problem (1–2 sentences), (d) Correct numeric answer, (e) Step-by-step solution showing each arithmetic step, (f) One-line note on why the scenario is realistic. Keep language simple, short sentences, Grade 6 reading level. Make each problem distinct; avoid near-duplicates.”
2. QA/Editor prompt (copy-paste):“You are a math editor. Review the 12 problems above. For each: 1) independently re-solve to verify the answer, 2) flag arithmetic errors, 3) check realism (typical prices/quantities), 4) check reading level and simplify if above Grade 6, 5) rewrite only if needed. Return the corrected set with Status per problem: OK or Fixed, plus a one-line reason for any fix. Keep the original numbering and the same output fields.”

Expect 1–3 items marked “Fixed” on the first pass. If more, tighten number ranges or contexts and rerun just those.

The problem: Volume is easy. Consistency isn’t. Without a simple quality gate, you get duplicates, off-level language, and arithmetic mistakes that erode trust.

Why this matters: Reliable, real-world problems increase learner confidence and reduce your editing time. Every 10% drop in error rate saves rework and preserves credibility.

Lesson learned: Treat this like production. Define constraints up front, force variety, and insert a QA pass that doesn’t rely on you doing long checks.

What you’ll need

• A short constraints card: grade, skills, number ranges, allowed contexts, reading level.
• An AI chat tool and a calculator for spot checks.
• 10 minutes of quiet time per batch of 12.

Operational steps (repeatable)

1. Define a Variety Matrix: pick 4–6 contexts (e.g., shopping, cooking, travel, sports, home projects) and 2–3 skills (e.g., fractions of quantities, percent of a number, percent change). This yields 12–18 unique cells. Generate one problem per cell to avoid duplicates.
2. Run the Generator prompt with your matrix and constraints card. Ask for labeled fields so you can scan fast.
3. Run the QA/Editor prompt. Only accept items marked OK or Fixed with clear reasons.
4. Spot-check 2 problems with a calculator. If either fails, tighten constraints and rerun the “Fixed” items only.
5. Package: group by skill and difficulty (easy/medium) and add one-line teaching notes: “Watch out for percent-as-whole mistakes.”
6. Scale: repeat 3 batches to reach 36–54 problems in under an hour.

KPI dashboard (simple targets)

• Arithmetic accuracy: ≥95% pass rate after QA (spot-check 10%).
• Variety coverage: ≥90% of matrix cells filled without near-duplicates.
• Reading level: within ±1 grade of target.
• Production speed: ≥1.5 finalized problems/minute.
• Rework rate: ≤15% of items require a second fix after QA.

Insider upgrades (premium)

• Locale realism: add “Use prices between $1–$50 and common pack sizes (e.g., 500 g, 1 L).” This cuts unrealistic outputs by half.
• Misconception tagging: require one common mistake per problem (“treating 35% as 35”) so you can teach proactively.
• Difficulty ramp: specify “Problems 1–4 easy, 5–8 medium, 9–12 mixed-step,” to control cognitive load.
• Refresh without rewriting: ask for “three numeric variants” per good problem to create fresh practice while keeping structure.

Common mistakes and fast fixes

• Duplication: If two problems share the same structure with new numbers, instruct “Each problem must use a different verb and scenario.”
• Overly complex language: Add “short sentences, avoid clauses, everyday words.”
• Unrealistic contexts: Set price and quantity bands; name everyday settings (supermarket, bus ride, home kitchen).
• Arithmetic slips: Force the model to show each step and run the QA/Editor prompt. Rerun only the flagged items.

One-week action plan

1. Day 1: Build your constraints card and 12-cell Variety Matrix. Run the 12-problem quick win loop once. Track accuracy and time.
2. Day 2: Add misconception tagging and difficulty tiers. Produce 24 more problems. Aim for ≥95% accuracy and ≤15% rework.
3. Day 3: Localize prices/units. Generate 24 more. Group by skill for a mini-unit.
4. Day 4: Create 2–3 numeric variants for your best 12 problems. You now have ~60–96 items.
5. Day 5: Pilot with 3–5 learners. Track first-attempt correctness and any confusing wording.
6. Day 6: Trim or rewrite items that underperform. Replace with new items targeting the same matrix cells.
7. Day 7: Final pass: sanity-check 10%, export your set, and note next week’s improvements.

Advanced prompt you can reuse with fill-in fields

“Create [N] Grade [X] real-world math word problems practicing [skills]. Use contexts: [list]. Number ranges: [min–max], no negatives. Output fields: Problem #, Context, Problem (1–2 sentences), Correct numeric answer, Step-by-step solution (each arithmetic step), One-line realism note, One common student mistake. Reading level: Grade [X], short sentences. Make problems distinct and cover each context-skill combination at least once.”

Lock the loop: constraints → generate → QA → spot-check → package. Track the KPIs above. Within a week, you’ll have a reliable pipeline that produces classroom-ready, real-world math problems at scale.

Your move.

[Fiona Freelance Financier]

Nice — your two-step generator + QA loop is exactly the quality gate most people skip. That simple production-minded approach is the best way to keep volume without adding stress.

To reduce the mental load further, use a short, repeatable micro-routine that turns every batch into a quick win. The method below keeps decisions small, checkpoints automated, and edits limited to obvious errors.

What you’ll need

1. A one-page constraints card: grade, target skills, number ranges, allowed contexts, reading level, and difficulty labels (easy/medium/hard).
2. A Variety Matrix of 12 cells (contexts × skills) so each problem has a unique slot.
3. An AI chat tool you already use and a basic calculator or spreadsheet for spot-checks.
4. 10–15 minutes of focused time per batch of 8–12 problems.

How to run a 10–15 minute micro-session

1. Load your constraints card and pick 8–12 matrix cells you want to fill (one per cell).
2. Ask the AI to generate one problem per chosen cell, requesting: a short scenario, the numeric answer, and explicit arithmetic steps. (Keep the instruction conversational — you don’t need a long scripted prompt.)
3. Run the AI’s output through a quick QA pass: have the AI re-solve each problem in a fresh message or export answers into a spreadsheet formula to auto-verify arithmetic.
4. Accept items flagged OK. For items flagged Fixed or showing arithmetic/realism issues, re-request a corrected version for that specific item only.
5. Group the verified problems by skill and difficulty, add one-line teaching notes where helpful, and save the batch to your library.

What to expect and simple targets

1. First-pass usable rate: ~70–90%. After one quick correction round you should hit ≥95% usable.
2. Time per batch: ~10–15 minutes for 8–12 problems when you follow the micro-routine.
3. Keep a short KPI: spot-check 10% of each week’s problems — that small habit prevents most errors.

Fast fixes for common failures

• Math errors: require the AI to show each arithmetic step and run an auto-verify in a spreadsheet, or ask the AI to re-solve independently.
• Duplicates: enforce unique context verbs and force one problem per matrix cell.
• Unrealistic numbers: tighten ranges on the constraints card (e.g., prices $1–50, pack sizes 250–1000 g).
• Language too hard: request short sentences and replace complex words before saving the batch.

Keep the loop tiny: pick cells → generate → auto‑QA → accept or fix → save. That small routine protects your time and keeps the problem set trustworthy — you’ll scale without stress.

[Author]

Posts