WeSolve+ is best for a student whose material is a file rather than a typed list, because that is exactly the line a text rule cannot cross.
WeSolve+ reads the whole document and writes the questions for you
Upload your PDF, photograph your notebook, or point the camera. WeSolve+ writes questions from that material, explains why each answer is right, reads the chapter back to you as a podcast, and remembers every item you missed until you own it.
The tool below is a small browser-only tool and it is not WeSolve+: paste a few lines and text rules turn them into cards on the spot. The real app, the one that uses AI, is behind the link above.
Text to quiz questions
This is a browser-only tool, and that is all it isIt splits the text you paste by rule, and nothing else. WeSolve+ is a different thing entirely: it reads your whole PDF with AI, writes the reasoning behind every question, speaks the chapter back to you, and remembers what you missed so it can return it. Try the real app now, free!
This tool uses text rules, not AI, so it converts rather than generates. It keeps a question with its options, matches an "Answer:" line to the item above it, and turns a plain statement into a gap fill. What it cannot do is read a file and decide which sentence deserves a question, which is the whole of the generating job. Your text stays in the browser and is never sent anywhere.
Reading a file is three problems, not one
A PDF is a description of marks on a page rather than a document with a structure, which is why extracting text from one is harder than it looks. First the characters have to be recovered, and where the page is a scan or a photograph that means optical character recognition with its own error rate. Second the reading order has to be worked out, which is layout analysis: two columns, a caption, a footnote and a header all arrive interleaved unless something reconstructs the flow. Only then does the third problem start, which is deciding what any of it means.
What generating adds, and it is a judgement
Given a chapter, a generator has to pick which twenty of its four hundred sentences deserve a question, then write a stem that has one answer and options that are wrong for a reason. That is selection plus authoring, and both parts need a model of the subject rather than of the text. The same machinery is studied under question answering and, more broadly, natural language processing. A rule can do none of it: the tool above will faithfully turn your sentence into a gap fill, but it has no way to know that the sentence was an aside and the important claim was in the next paragraph.
The three failures worth watching for
Generated questions fail in predictable ways and knowing them is most of the defence. Trivia: the model asks about a date in a caption because dates are easy to make questions from, not because the date matters. Leakage: the question repeats the source sentence so closely that the answer is visible in the wording. And confident invention, the failure called hallucination, where an option or an explanation states something your source never said. The practical check takes a minute: read five generated questions against the page they came from before you trust the other fifteen.
Why explained output matters more than the count
A generator that returns fifty bare questions gives you no way to audit it. One that shows its reasoning lets you see which sentence it drew on, which turns an unverifiable claim into a checkable one. This is also the difference between an answer and a lesson: when you get an item wrong, the reasoning tells you where your model of the topic diverged. Numbers are a poor proxy for quality here, and any product advertising how many questions it produces per upload is advertising the easy half. Large language models will generate as many items as you ask for; being able to check them is what is scarce.
When converting is genuinely the better choice
If the questions already exist, generating new ones is worse than reusing them. A past paper, a problem set, a marking scheme and the end of chapter questions were all written by people who know what the exam asks, and no generator improves on that. Converting them costs nothing, keeps their wording, and is what the tool above does. The rule of thumb is simple: if you can copy the questions, convert them; if you have a source but no questions, that is when generating earns its place. There is a fuller version of the same distinction on the flashcard generator page, which is this argument applied to cards.
What the app generates from one file
Upload a PDF, a photograph of a page or a camera shot and WeSolve+ reads it and writes an explained quiz, a card deck and a spoken recap of about a minute from that single file. Explained is the part this page has argued for: the reasoning is attached, so an item can be checked against the source rather than trusted. Questions you miss are kept in their own list. It runs at wesolveapp.com in any browser and as an App Store app on iPhone and iPad, free to begin, with the meter on files rather than on questions. There is no separate Android app. Figures on pricing.
Sources used on this page
- Optical character recognition
- Document layout analysis
- Natural language processing
- Question answering
- Large language model
- Hallucination in artificial intelligence
- Multiple choice
- Item analysis
- Educational assessment
- Cloze test
- Active recall
- Testing effect
- Spaced repetition
- Roediger and Karpicke, Test-Enhanced Learning (2006)
- Karpicke and Roediger, the critical importance of retrieval (2008)
- Cepeda et al., distributed practice meta analysis (2006)
- Dunlosky et al., improving students' learning (2013)
- What Works Clearinghouse, organizing instruction and study
- The Learning Scientists, retrieval practice
- Retrieval Practice, the research library
- Anki manual, importing text files
- MDN, the textarea element
- MDN, the details element
- MDN, the Clipboard API
- W3C, the disclosure pattern
- W3C, Web Content Accessibility Guidelines 2.2
- WeSolve+ on the App Store
- Machine learning
- Information extraction
- Text mining
- Artificial intelligence
| Step | Converter, the tool here | Generator, the app |
|---|---|---|
| Input | Questions you typed or pasted | A PDF, an image or a camera shot |
| Character recovery | Not needed | Text layer or optical recognition |
| Reading order | Your line breaks | Layout analysis, columns and captions |
| Choosing what to ask | You already chose | The model chooses, and can choose badly |
| Writing wrong options | Kept from your text | Written, and worth auditing |
| What you should check | Nothing, it is your text | Five items against the source page |
Is the tool on this page an AI quiz generator?
No, and saying otherwise would be a lie. It converts questions you already have using text rules. Generating means reading a source and deciding what deserves asking, which needs a model.
Why can a rule not read a PDF?
Because a PDF describes marks on a page rather than a structured document. Recovering characters, then reading order, then meaning are three separate problems, and a browser rule solves none of them.
How do I check generated questions?
Read five of them against the page they came from. The failures are predictable: trivia from captions, questions that leak their answer in the wording, and options stating something the source never said.
Is more questions per upload better?
No. Generating many items is the easy half; being able to audit them is the scarce part. Explained output beats a larger count you cannot check.
When should I convert rather than generate?
Whenever the questions already exist. A past paper or a problem set was written by somebody who knows the exam, and no generator improves on that.
Does anything I paste here get uploaded?
No. The conversion happens in your browser with no request leaving the page. Closing the tab ends it.
Last updated: 2026-08-15