by QuizGen (LX AI)
QuizGen turns a topic and difficulty into a set of questions with model answers — for teachers, tutors, and self-learners who want exportable quizzes without rewriting every item by hand.
Decision-support only — confirm before you publish or act.
I start with a short text description of the topic and a difficulty label (easy, medium, hard). The system sends this pair to a language model that has been prompted to produce a list of question‑answer pairs. The prompt asks for a specific number of items, a clear stem, and a model answer that cites the source material if any. The model returns raw text that I then parse into a structured format: each item gets an ID, a question string, an answer string, and a difficulty tag. This parsing step strips any extra commentary the model might have added and ensures the output matches the schema I use for downstream steps.
The generation loop runs until the requested number of valid items is collected. If the model returns fewer usable items than asked, I automatically request another batch with a slightly adjusted prompt (e.g., ask for more concrete examples). This retry logic keeps the process fully automated while still giving me a chance to intervene if the quality drops below a threshold I set.
QuizGen accepts topic descriptions as plain text up to 500 characters. Difficulty must be one of the three predefined strings: easy, medium, hard. I enforce this with a simple validation step that returns an error if the input does not match. The topic can include domain‑specific jargon; the model treats it as a signal for the content area it should draw from.
There are limits on the number of questions per request. The minimum is five and the maximum is fifty. These bounds come from testing the model’s consistency: below five items the output feels too sparse for a quiz, above fifty the chance of repetitive or off‑topic items rises sharply. I keep the limits in the API documentation and in the UI so users know what to expect.
If a user supplies a topic that is too vague (e.g., “math”), the system asks for clarification by returning a suggestion to add a subtopic or a grade level. This feedback loop reduces wasted generations and helps the user get a useful set on the first try.
After generation, QuizGen shows each item in a card layout. The card displays the question, the model answer, and a difficulty badge. I provide three actions per card: edit, delete, or flag for review. Editing opens a simple text box where the user can change the question wording, the answer, or both. Deleting removes the card from the list. Flagging adds a comment field where the user can note why the item needs a second look (e.g., answer seems inaccurate, question is ambiguous).
I also include a bulk‑edit mode. By selecting multiple cards the user can apply a common change, such as updating the difficulty tag for all selected items or appending a standard phrase to every answer. This mode saves time when a systematic tweak is needed after the first pass.
Before moving to export, the user can run a quick consistency check. The tool scans the set for duplicate questions (exact string match) and warns if any are found. It also checks that every answer is not empty and that the difficulty distribution matches the user’s original request (within a tolerance of ±10%). These checks are optional but I recommend running them to catch obvious slips.
QuizGen can produce several file formats that work with common learning management systems and authoring tools. The table below shows the formats, the file extension, and the typical use case.
| Format | Extension | Typical use |
|---|---|---|
| CSV | .csv | Import into most LMS quiz builders or spreadsheet programs |
| JSON | .json | Custom integrations, API consumption, or further processing |
| QTI 2.1 | .xml | Standard package for platforms like Canvas, Moodle, Blackboard |
| Plain text | .txt | Quick review or printing for paper‑based quizzes |
When the user chooses CSV, each row contains four columns: id, question, answer, difficulty. The JSON export nests each item under a “questions” array with the same fields. The QTI output follows the specification for assessment items, including the correct response identifier and optional feedback text. The plain‑text export lists each question followed by its answer on the next line, separated by a blank line.
All exports preserve the difficulty tag so the user can filter or sort later. I do not modify the content during export; what the user sees in the review screen is exactly what gets written to the file.
QuizGen does not push quizzes directly into a learning management system. Instead, it creates a file that the user can upload through the LMS’s import feature. I have tested the CSV and QTI outputs with the latest versions of Moodle (4.2), Canvas (2024‑03), and Google Forms (via the CSV import add‑on). The import succeeds when the LMS expects the standard column order or QTI structure.
For authors who use tools like Articulate Rise or Adobe Captivate, the JSON format works well because those platforms allow custom scripts to read JSON and generate quiz slides. I provide a short example script in the documentation that shows how to fetch the JSON, loop over the items, and create a slide with a question component and an answer reveal.
If the user needs a different format, they can request it through the feedback form. I evaluate each request based on how many users would benefit and whether the format has an open specification.
When building a tool that relies on a language model, I pay attention to known failure modes. The OWASP Top 10 for LLM Applications (https://owasp.org/www-project-top-10-for-large-language-model-applications) highlights risks such as prompt injection, data leakage, and unsafe output generation. I mitigate prompt injection by keeping the user‑supplied topic and difficulty separate from the instruction template; the model never sees the raw input as part of the command that controls its behavior.
To address the risk of producing inaccurate or biased content, I follow the NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework). The framework suggests steps like testing with diverse inputs, logging model responses, and providing clear disclaimers. I log each generation request (topic, difficulty, timestamp) without storing the actual question text, which helps me spot patterns of repeated failures. The disclaimer at the top of the interface reminds users that the output is decision‑support only and they must verify correctness before publishing.
I also limit the model’s temperature to a low value (0.2) during generation to reduce randomness, and I set a maximum token limit that prevents the model from wandering into unrelated topics. These technical controls, combined with the human review step, keep the reliability of the produced quizzes at a level that matches the expectations of educators who need a starting point rather than a final product.
QuizGen does not guarantee that every generated question is factually correct. The model may produce statements that sound plausible but are inaccurate, especially for niche or rapidly changing topics. I do not claim 100% accuracy, and I never assert that the tool will never miss an important concept.
The system is not a replacement for expert review. It is meant to reduce the manual effort of drafting the first version of a quiz, but the final quality depends on the user’s subject‑matter knowledge and their willingness to edit or discard items.
QuizGen does not provide adaptive testing algorithms, analytics on learner performance, or integration with proctoring services. It focuses solely on the creation of static question‑answer sets that can be exported and used elsewhere.
Finally, the tool does not store or share the content you create beyond the temporary session needed to generate the file. I do not retain your topics, questions, or answers on any server after you close the browser tab.
I keep a written checklist next to every release that touches this topic. The list is boring on purpose: name the owner, name the evidence artifact, and name the rollback. If a step cannot produce a file, a ticket, or a log line, it is not a step — it is a wish.
| Signal | What to record | Stop-ship? |
|---|---|---|
| Unexpected write or send | Tool name, args, identity | Yes until gated |
| Missing confirmation on destructive action | Call path and bypass | Yes |
| Untrusted text treated as instruction | Source surface and resulting action | Yes for agent reach |
| Scanner clean, judgment open | Deferred items + owner | Only if core path safe |
quiz-gen helps with the measurable first pass. Architecture choices, legal sign-off, and customer-facing claims still need a human owner. Treat every checklist row as decision support, not a certificate.
When a prospect asks whether the product "covers everything," I answer with the boundary: automated checks catch known patterns; novel chains and policy judgment still need review. That honesty keeps support load down and keeps the blog aligned with what the product actually does.
If you need a second opinion, pair this write-up with the references below and with your own staging reproduction. Do not skip the reproduction step just because a model wrote a confident summary.
QuizGen turns a topic and difficulty into a set of questions with model answers — for teachers, tutors, and self-learners who want exportable quizzes without rewriting every item by hand.
Decision-support only — confirm before you publish or act.
Want to try the product? QuizGen
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