Pīzhù (批注, “margin notes”) is a writing-tutor project designed around a simple distinction: it points to a passage, explains a potential issue in English and Chinese, and offers a hint rather than rewriting a student’s essay. Its author built it for Joy, a Mandarin-speaking MA student in Global Mass Communication in Ireland. The design may help a student understand feedback without handing over their voice, but the available account does not establish that Pīzhù improves grades or learning outcomes.
What Pīzhù does differently from a rewriting chatbot
The project’s author describes Pīzhù as a tutor that leaves the revision to the student. Instead of returning a polished replacement paragraph, it highlights a specific span, explains a pattern the student may want to address, and suggests a limited nudge. The intended sequence is: notice the passage, understand the feedback, then make the change yourself.
That approach is particularly relevant to Joy’s stated problem. In the author’s account, she had received feedback summarized as “It’s Just bad english”. Pīzhù is meant to make that kind of response more actionable by explaining why a sentence may be difficult to follow or need attention. The product’s phrase “Margin notes, not rewrites” describes its positioning; it is not an independent finding about its effectiveness. Read the project author’s account.
How the author says the tutor limits rewrites
The author describes an implementation that combines model-generated feedback with validation code. The agent returns structured data, and the described schema has no field for a rewritten essay. A guard checks whether quoted or highlighted text appears in the student’s submission; it also limits nudges that exceed a stated word-edit threshold and rejects nudges that cross sentence boundaries. These are implementation claims from the author’s account, not the results of an independent code audit.
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The design can reduce opportunities for the model to substitute a complete essay, but it does not prove that every suggestion is correct, that no unwanted rewrite can occur, or that the feedback is pedagogically effective. Students still need to review each note and decide whether it fits their intended meaning and assignment.
What features the project account describes
- Explanatory annotations: notes tied to selected text, with explanations in English and Chinese.
- Lesson mode: an interface option described as showing one note at a time, which can keep attention on a single revision.
- Word comments: export to a Word document with comments, according to the author.
- Reference checking: a workflow described as combining model-based reference extraction, code-based matching, and Google Scholar searches through SerpApi. A search workflow is not proof that a citation is genuine or correctly represented.
- Longer-paper support: the author says the app was updated with a whole-paper overview and an assignment-brief explanation after Joy described difficulty getting advice on a dissertation in pieces.
- British spelling: a rule-based layer is described alongside the model-generated feedback. The project is intended for British and Irish academic contexts, but that does not establish suitability for every institution’s style rules.
The article’s publication-time account mentioned a hosted-demo limit that changed from 1,500 to 6,000 words, while saying the local version had no such limit. Those figures describe that account at publication, not a verified current service limit or guarantee of present availability.
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Where Gemma 4 fits—and what it does not prove
The project author says Pīzhù can run locally through Ollama with Gemma 4 E4B or 12B, or use Gemma 4 26B A4B for a hosted demo through Google AI Studio. The author also says an environment variable switches between those modes. Google’s Gemma 4 model card independently identifies Gemma 4 as an open-weight family released under Apache 2.0, with E2B, E4B, 12B, 26B A4B, and 31B variants.
Google documents text and image input across the family, and audio input for E2B, E4B, and 12B Unified. It lists 128K-token context windows for E2B and E4B, and 256K for 12B, 26B A4B, and 31B. These are model-family specifications, not a statement of how much text Pīzhù can process in a particular deployment. The model card also describes multilingual support and configurable thinking; neither establishes the quality of feedback for Mandarin-speaking students writing academic English.
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Google’s prompt-formatting guide explains the turn and control-token structure used in Gemma prompts. That general documentation provides context for structured model interactions, but it does not verify Pīzhù’s particular architecture. Google also cautions that Gemma models can struggle with ambiguity, nuance, factual accuracy, and complex tasks—limitations that matter when judging writing notes or checking references.
Local and hosted use are different trade-offs
| Option described by the author | Model and setting | What the account says | Important qualification |
|---|---|---|---|
| Local | Gemma 4 E4B or 12B through Ollama | The author says coursework stays on Joy’s laptop and the local version has no word limit. | These are claims about the described setup, not a general privacy audit or a promise about every installation. The actual limits depend on the local app and hardware. |
| Hosted demo | Gemma 4 26B A4B through Google AI Studio | The author says this mode supports the public demo. | Demo availability and limits can change; the publication-time word-limit figures should not be treated as current. |
Google’s documented context windows help distinguish model variants, but context capacity alone does not determine whether a long paper is handled well: the app’s configuration, workflow, and ability to preserve the full argument also matter. The available evidence does not establish a best variant for every student.
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What Joy’s feedback and the reported evaluation show
The author attributes positive feedback to Joy, who said: “The general feeling of this app: it’s very useful, especially for our module assignments. The Chinese explanation is easier to understand than the criteria written in our module 😂” This is one learner’s account, not a representative study. Joy also said that splitting her dissertation into sections made it difficult for the tool to understand her full argument; the author says that feedback led to the whole-paper overview and assignment-criteria features.
The author reports evaluation results of 84.2% for E4B, 94.7% for 12B, and 89.5% for 26B-A4B, as well as separate latency figures and instances where the guard rejected or constrained generated nudges. The account does not establish an independent evaluator, sample size, test-set construction, scoring rubric, or replication. These percentages therefore should not be read as standardized benchmark scores or a reliable ranking for other students’ work.
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A separate Google article, published June 9, 2026, describes BetterSpeak, an offline English tutor using quantized Gemma 4 E2B on-device with audio input. It is an adjacent example of an education use case, not evidence that Pīzhù uses the same setup or produces effective feedback. Google’s BetterSpeak article.
Who should consider a tutor like this
Pīzhù’s design is most relevant to a student who wants explanations and prompts for revision rather than an AI-generated replacement essay. Its focus on Joy’s experience—Mandarin explanations, academic assignments, and British or Irish conventions—is a specific design choice, not a claim that all Mandarin-speaking students have the same needs or that the tool has been validated across universities.
Anyone trying it should treat annotations as suggestions to evaluate, especially where meaning, assignment criteria, or references are involved. The sources available for this account do not establish improved grades, prevention of academic misconduct, or reliable detection of fabricated references. Google’s documented model limitations reinforce why feedback should remain reviewable rather than authoritative.
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