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Case study
A dissertation lit review that stays connected end to end
A PhD candidate takes a background chapter from scattered searches to a cited draft, with the papers he finds flowing into his review and his writing.
AcademicAI SearchResearch AgentLiterature ReviewAI WriterReference ManagerChat with PDF
The papers I save are the ones my literature review pulls from, and the ones my writing cites. I stopped rebuilding the same reference list in three different places.
Panoramica
Tom is a PhD candidate in cognitive science writing his dissertation and staying current across a fast-moving subfield.
La sfida
His research lived in five disconnected tools, search, PDFs, a spreadsheet, a reference manager, a document, and the context he built in one was lost in the next, while deadlines stayed fixed.
Come si inserisce Paperguide
Paperguide connects the whole arc: papers found in search save to his library, feed his review, and become cited sources in his writing, without moving files between tools.
Nella pratica
Tom’s dissertation work on working memory and attention control was scattered across the usual five tools, a search engine, a folder of PDFs, a spreadsheet, a reference manager, and the document he was actually writing in, and the context he built in one kept evaporating in the next. The reference list he assembled for his reading was not the one his review drew on, which was not the one his writing cited; he was rebuilding the same list three times.
In one workspace, the pieces connect. He orients himself with cited answers from AI Search and lets the Research Agent map the themes, agreements, and open questions in the subfield. When he’s ready to draft the background chapter, the Literature Review Agent produces a protocol-first, themed synthesis he can use as a working draft, and the AI Writer drafts sections from his own library with the citations already placed, so writing is editing rather than hunting for references. The dense computational-modeling papers that used to stall him, he works through by questioning them directly. Crucially, the papers he saves along the way are the same papers his review pulls from and his writing cites, one connected library, shareable with his supervisor.
The benefit isn’t a single dramatic number; it’s the removal of a hundred small frictions. He orients faster in a moving field, and he stops rebuilding the same reference list in three places, which is exactly the tax that makes dissertation writing feel slower than the thinking behind it.
Le funzionalità al lavoro qui: AI Search, Research Agent, Literature Review, AI Writer, Reference Manager, Chat with PDF.
I risultati
- A background chapter drafted from his own collected, cited papers
- One connected library from search to writing, no duplicated reference lists
- Faster orientation in a fast-moving subfield
Altri case study
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