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Pīzhù (批注, “margin notes”) is an open-source writing-tutor project built by its author for Joy, a Mandarin-speaking MA student in Global Mass Communication in Ireland. Instead of producing a replacement essay, it is designed to highlight specific passages, explain what may be wrong in English and Chinese, and offer a hint or limited nudge so the student can revise the work herself. That is the project’s stated design—not independent evidence that it improves writing or learning.
What Pīzhù is designed to do
The project responds to a familiar problem: a marker flags a sentence, but the feedback does not explain what to change. Joy described faculty feedback as “It’s Just bad english”. Pīzhù’s intended answer is a margin note that identifies a pattern and explains it, rather than silently replacing the sentence or paragraph.
The author presents the app as intended for British and Irish academic contexts and tailored to one learner’s needs. The examples of writing patterns it targets should not be read as traits shared by all Mandarin-speaking students.
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Annotation rather than rewriting
According to the author, the tutor returns structured feedback without a rewritten-essay field. A note can quote or highlight text, describe the issue, and provide a hint or tightly limited nudge. The student remains responsible for deciding whether and how to revise.
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The author says code checks that a quoted span appears in the submitted text, restricts nudges that exceed a stated word-edit threshold, and rejects nudges that cross sentence boundaries. These are descriptions of the project’s safeguards, not an independent audit or proof that every response preserves a student’s voice.
English and Chinese explanations
Joy said the Chinese explanations were easier to understand than her module criteria and helped her see why a sentence needed correction. Her response was: “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 user’s feedback, not a representative study.
How the project is described as working
The developer’s account describes a combination of model-generated feedback and conventional code: structured model output, a validation schema, deterministic guard checks, and a rule-based layer for British spelling. The stated goal is to constrain the assistant to explain and annotate rather than return an essay rewrite.
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The project article also describes lesson mode, which can present one note at a time, and exporting a Word document with comments. Its reference-checking workflow is described as using the model to extract reference details, code to match them, and Google Scholar searches through SerpApi. A suggested reference or a match should still be checked by the student; Google warns that Gemma models may make factual errors, and the available evidence does not establish reliable detection of nonexistent references.
Gemma 4: what it contributes, and what it does not prove
The author reports running Pīzhù locally through Ollama with Gemma 4 E4B or 12B, and using Gemma 4 26B A4B for a hosted demo through Google AI Studio. The author says an environment variable switches between those modes. These are details about this project as described by its developer, not a guarantee about the current demo or every installation.
Google’s Gemma 4 model card identifies the family as open-weight and licensed under Apache 2.0. It lists E2B and E4B, 12B, 26B A4B, and 31B variants. The documented context windows are 128K tokens for E2B and E4B, and 256K for 12B, 26B A4B, and 31B. Those are model specifications; they do not establish how much text Pīzhù’s interface accepts or how well it handles a particular dissertation.
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The model card describes text and image inputs across the family, with audio support on E2B, E4B, and 12B Unified, as well as multilingual capabilities. It names language-learning uses such as grammar correction and writing practice, but this general capability information is not a test of Pīzhù or proof of quality for Mandarin-to-English academic writing. Google also cautions that Gemma can struggle with ambiguity, nuance, complex tasks, and factual accuracy. Treat its comments as suggestions to assess, not authoritative corrections.
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Local and hosted use are different trade-offs
The author says the local setup keeps Joy’s coursework on her laptop, while the hosted setup supports a public demo. That is the developer’s description of these configurations, not a privacy audit or a guarantee for other deployments. A student should check where documents are processed and stored in the specific version being used, and follow their institution’s rules before submitting coursework to any hosted service.
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Local use also depends on configuring the software and running a compatible model; hosted use depends on the demo or service being available. The author’s article described a hosted-demo limit that changed from 1,500 to 6,000 words, while saying the local version had no such limit. Those are publication-time details and should not be treated as current availability or limits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Long-paper support and the limits of the evidence
Joy said that working on her dissertation in separate sections made it difficult for the tool to understand her full argument: “As my dissertation is a long paper, when I tried to ask advice from momo, I have to send my paper separately, even need to split each chapter to 2-3 parts. Thus, momo cannot get the full understanding of my argument. If the app could do longer articles, it would be great!” The author says the app was subsequently updated with longer-paper support, a whole-paper overview, and a feature for explaining assignment criteria. The feedback and feature history come from the developer’s account.
The same account reports evaluation results of 84.2% for E4B, 94.7% for 12B, and 89.5% for 26B-A4B, along with latency figures and cases where the guard rejected or constrained nudges. Without an established sample size, test-set design, scoring rubric, independent evaluator, or replication, those percentages are the author’s reported results—not standardized benchmark scores and not a reliable ranking for other students or tasks.
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The available sources do not establish that Pīzhù raises grades, prevents academic misconduct, or reliably detects fabricated references. They also do not provide an independent study of learning outcomes. Its strongest evidenced distinction is its intended interaction: explanations and annotations in place of a generated replacement essay.
Who might find the approach useful
Pīzhù’s design may suit a student who wants to understand feedback and make revisions personally, particularly if bilingual explanations make academic criteria easier to interpret. It is less suited to anyone seeking a finished rewrite or a substitute for a supervisor, course rubric, or careful source checking. The project’s framing is specific to Joy’s experience; whether the same workflow helps another writer depends on their needs and on the quality of the feedback they receive.
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