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Exam Buddy turns a student’s own course notes into multiple-choice, written-answer, or mixed quizzes. Its author, Kamal, built it to help a friend study for an Operating Systems exam, using Gemma 3 4B through Ollama for local inference. The project pairs model-generated questions and feedback with application logic intended to handle malformed output and other reliability problems.
What Exam Buddy does
Instead of generating generic Operating Systems trivia, Exam Buddy uses the learner’s notes as its source material. That matters when a course emphasizes particular concepts, uses shorthand, or includes a professor’s comments that may not appear in a general-purpose quiz.
Kamal’s project article describes three quiz formats:
- Multiple choice: answer from generated options.
- Written answers: respond in your own words and have the model grade the response.
- Mixed: combine the two answer styles.
The learner can choose 5, 10, or 15 questions. Other features described include themed explanations, follow-up questions, a retry mode for missed questions, score history, and a generated summary of what to review. Long notes are split into parts so that separate quizzes can be made from them.
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How the local quiz tool is put together
Kamal identifies Gemma 3 4B as the application’s only AI model and Ollama as its local inference route. The project began as a Python command-line script and later gained a browser interface. In the web version described in the article, a self-contained HTML file sends requests to Ollama’s local API with fetch; the page is served with python -m http.server.
The author describes this version as having no separate backend, framework, build step, or API keys. His setup summary is to install Ollama, pull Gemma 3 4B, and serve the web interface. That is a project overview, not a complete installation walkthrough; Kamal points readers to the repository README for detailed setup instructions.
What the author had to make more reliable
The project narrative makes clear that generating a quiz is not just a matter of asking a model for questions. The application has to validate user input, interpret generated output, and recover when either step goes wrong. Kamal reports several changes made during development:
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Invalid answers and malformed output
The early command-line program crashed when someone submitted a blank answer because it tried to parse an empty string as an integer. The author says he fixed that case with a validation loop that accepts only numbered choices and prompts again otherwise.
Model responses also sometimes contained malformed JSON, including JSON wrapped in Markdown code fences. The implementation added retries, cleanup, and object extraction as recovery measures. These are safeguards for the application’s expected output format, not a guarantee that every response will be usable.
Long notes and predictable answer positions
In an early version, a 16 KB note file proved too much for the 4B model to turn reliably into structured questions, according to Kamal. He responded by splitting notes into parts. He also noticed that correct options appeared repeatedly in the same position, so the application shuffles answer choices after generation.
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Written-answer grading and request handling
Kamal reports that the written-answer grader once gave an “idk” response a score of around 75% while providing feedback that contradicted that score. The account illustrates why a numeric grade should not automatically be treated as dependable evidence of mastery. The project also received changes for a streaming-request race and for validation that rejected some otherwise usable question batches.
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Prompt injection: one reported anecdote
The article mentions an accidental prompt-injection test, but that single anecdote does not establish that the grader is resistant to prompt injection. It should not be read as a security test or as evidence that hostile instructions in notes or answers cannot affect results.
All of these are observations from the author’s development account, not controlled benchmarks. They describe this project’s behavior and fixes; they do not establish how the model will perform with other hardware, subjects, or study materials.
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What “local” means for privacy
Kamal says inference happens locally through Ollama, so notes and answers do not need to be sent to a remote AI API. Once Ollama and the model are installed, he says the tool can work offline. For this personal project, local inference also avoids per-request API charges and API-key management.
That is a narrower privacy claim than saying the application is secure or private in every respect. The article does not establish that the full machine environment is secure, that the software has been audited, or that data cannot be exposed through other components or computer use. The supported point is that the AI inference path described by the author does not depend on sending study material to a remote AI API.
Who might find Exam Buddy useful
The design is aimed at someone who has course notes and wants practice questions grounded in those materials, especially when generic questions would miss a class’s particular emphasis. Multiple-choice mode offers recognition practice; written mode asks the learner to formulate an answer; mixed mode combines them. Review features such as retrying missed questions and seeing a “what to review” summary support another pass through weak areas.
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Because the project’s author reports a grading inconsistency, treat model-graded written answers as practice feedback rather than authoritative assessment. Checking a questionable grade against the notes or a course reference is sensible, particularly when the explanation and score do not agree.
Source and scope
The project details and development anecdotes above come from Kamal’s account, “Exam Buddy” on DEV Community, published October 5, 2026. The article describes one personal project; it does not provide independent testing, comparative model results, or hardware requirements.
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