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A study buddy can answer questions from class notes without sending those notes to a cloud AI—but the promise depends on where every step runs. In a genuinely local design, the app extracts and indexes the notes on the device, retrieves relevant passages for each question, and sends those passages to a model running on that same device. A model download during setup or a network-enabled feature can still create traffic, so “never uploads” needs a clear scope and evidence.
How does an AI study buddy read notes on the device?
A document assistant typically combines a document reader, a search index, and a language model. The reader turns a file into text; the index helps find relevant passages; and the model uses those passages to draft an answer. This is often called retrieval-augmented generation, or RAG.
- Import: The student selects a document, such as a PDF. In a local-only implementation, the file is read from the device rather than uploaded to a service.
- Extract: The app reads embedded text. Scanned pages may need optical character recognition (OCR), which converts page images into text. Both steps must run locally for the document to remain on-device.
- Index: The app divides the extracted text into smaller sections and creates a searchable index. Some systems use embeddings—numerical representations that help match a question to relevant passages. These, too, should be generated and stored locally if the goal is to avoid sending note content out.
- Retrieve and answer: For a question, the app finds relevant sections and provides them to a language model. In a local design, the model runs on the student’s computer, and the answer can point back to the source passage or page.
Local retrieval can help an answer stay anchored to the course material, but it does not guarantee correctness. The model can misread a passage, miss relevant context, or make an unsupported inference. Check important answers against the cited page or passage.
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Examples of this architecture
ScholarOS describes a PDF assistant that parses and chunks documents, creates embeddings in a local vector store, and uses a local language model for questions, flashcards, and summaries. Its repository also describes laptop deployment, including low-resource machines; those are maintainer claims, not independent performance results. ScholarOS project repository.
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Kiosk-Scholar describes a desktop PDF assistant using local Ollama inference, page-linked summaries, passage explanations, and offline text-to-speech. Its statement that inference is local and nothing leaves the machine describes that project; it does not verify another app’s behavior. Kiosk-Scholar project repository.
What does “never uploads” actually mean?
It is a claim about data movement, not just where an app saves its files. “Stored locally” means a copy is kept on the device. “Not used to train models” describes a provider’s training policy. Neither proves that a document is never transmitted for remote processing.
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For example, Microsoft says its Study and Learn Agent processes uploaded messages and documents in real time within the Microsoft 365 tenant, does not use content or conversations to train the underlying models, and may collect basic usage telemetry. It can cite uploaded material in grounded answers. That is a cloud-processing model with stated privacy protections—not a no-upload model. Microsoft’s Study and Learn Agent privacy and responsible AI information.
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Likewise, the StudyCraft/NoteSynthesis policy says notes and study materials are stored in browser IndexedDB, while content a user asks an AI feature to process is sent to its AI provider. The policy was last updated November 3, 2025. This vendor disclosure illustrates why browser storage alone does not establish that AI processing stays in the browser or on the device. StudyCraft/NoteSynthesis privacy policy.
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How can you check whether an app sends notes to the cloud?
For a particular build, the answer should come from its code, configuration, documentation, or network behavior—not from how its interface describes privacy. A useful check follows the data through the whole workflow:
- Import and parsing: Identify where files are read and whether OCR or another document service processes them remotely.
- Indexing: Check whether text, chunks, or embeddings are sent to a hosted search or vector service.
- Questions and answers: Find out whether prompts and retrieved passages go to a cloud model or to an on-device model.
- Logs and telemetry: Check whether document text, prompts, filenames, or other identifying details are recorded or transmitted.
- Backups, exports, and integrations: Account for sync, cloud backups, sharing, and optional services that may transmit content even when the core workflow is local.
- Setup and maintenance: Separate document processing from model downloads, app updates, and other network requests. An app may need the internet during setup but process notes locally afterward.
If testing the app’s traffic, record what was checked and under what conditions: for example, whether the test began after setup, which features were used, and whether the device was online. A period with no observed traffic is evidence only for that build and those tested actions; it is not proof that every version, setting, or optional feature behaves identically. Reviewing code paths and testing network behavior can complement each other, but neither should be described more broadly than it supports.
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Can a study buddy work offline?
It can, if the required document processing, retrieval, and model inference are available locally. Offline operation may depend on first downloading the application and model, and an app may still use the network for updates or optional features. Make the distinction explicit: “works offline after setup” is different from “never makes network requests.”
A local language model also needs to run on the host device. The right hardware depends on the particular model and workload; the project examples do not establish a universal device requirement or performance level. A laptop is a plausible host, but do not infer that a given laptop will run a particular model well without checking that model’s requirements.
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What should students compare before choosing an assistant?
| Question | Why it matters |
|---|---|
| Where do parsing and OCR happen? | Document contents may leave the device before a question is ever asked if extraction uses a remote service. |
| Where are indexing and inference performed? | A local file store does not establish that embeddings, prompts, or retrieved passages stay local. |
| What actions send data, if any? | Some products send material only when a user invokes an AI feature; sync and optional integrations may create other paths. |
| Does the answer identify its sources? | Page or passage references let students check whether an answer follows the assigned material. |
| What works offline, and what needs the network? | Setup, model downloads, updates, and optional services can have different connectivity requirements from ordinary study sessions. |
| Can the device handle the actual model and workload? | Local inference avoids a cloud model call, but depends on the host device and selected model. |
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