Day 1 | Define the Boundaries
Don't write sentences like "I want to write a literature review"; write "What question am I answering, what is the time range, and do methodological papers count?" Once this sentence is written, the search has direction. Use MuchSci's Paper Search Assistant to break this sentence into search dimensions, search by title, author, and DOI, and filter by year and citation count. When a match is found, the DOI and citation information (authors, journal, year, volume/issue/page numbers) are filled in automatically, so you don't have to copy them by hand.
Day 2 | Fill the Ledger
Create a literature ledger: ID, author-year, research question, methods, main conclusions, limitations, whether cited, and citation location. Have the tool break down each paper by research question, methods, conclusions, and limitations—these are exactly the four hardest columns to fill in the ledger. At this point, what you have is a table, not a pile of PDFs.
Day 3 | Layered Reading
Use the ledger to decide how to read each paper: read background reviews through to build understanding; for original papers that influence my conclusions, read the full text, focusing on methods, data, and supplementary materials. For English literature I use paragraph-level parallel reading (bidirectional Chinese-English academic translation, preserving formula and terminology consistency, with support for full PDFs), and check the original text when I reach key points. The cost of misreading one value is far greater than the cost of verification.
Day 4 | Structure
Reorder the ledger by theme, not by year. After reordering, find one shared conclusion and one point of disagreement in each group. The skeleton of the review is these "shared conclusions + points of disagreement," not a framework I invent and force onto it.
Day 5 | Draft and Self-Check
Self-check list: whether the terminology is consistent (Chinese translation + original English term), whether Chinese translations have been used as search terms, whether every value has been checked against the original text, whether the citation information is complete and verifiable, and whether translations have been treated as factual evidence and written directly into the main text. When I can't write, I first give the outline to the Office Assistant Agent to generate a first draft PPT (PPT/Word/Excel can all be generated from one sentence), then present it against a few slides: only these, does the main line hold? Where it doesn't hold is a structural problem.
Things accumulated over months and years—handwritten notes, whiteboard photos, lecture recordings—I archive after reading: translated drafts and key points go into the personal knowledge base (supports import of PDF, DOCX, PPTX, XLSX, TXT, MD; defaults to personal use and is not shared), those with mixed sources go through the personal database layer, and reports generated by AI note-taking can also be stored there. Scattered materials are as good as not accumulated.
Academic integrity has a very concrete place in this workflow: translations, values, and citations are all checked against the original sources; this part cannot be outsourced.
With this graduate student AI assistant, what I save is time on searching and filling out forms; there is nothing magical about the five-day allocation. The real difference is whether Day 1 and Day 2 are done seriously. The effort saved on these two days will be demanded back double on Day 4 and Day 5.