How I Use AI to Manage a Research Project: From Papers to a Revised Draft
I tested SciSpace's new Library Agent and Word editor to organize sources, compare findings, and revise an actual manuscript with tracked changes.
My research workflow is usually spread across too many places.
I find papers on platforms like arXiv, save the PDFs somewhere else, and use an AI chatbot when I need help understanding a dense section. Once I start writing in Word, nearly all that context stays behind. I have to move the useful findings manually, fix the formatting, check the citations, and repeat the process every time I revise the draft.
It works, but there is a lot of unnecessary back and forth.
SciSpace is an AI research platform for finding papers, reviewing literature, analyzing PDFs, managing citations, and writing research documents.
The tool recently added two features that could make this workflow easier.
A Word editor that can open and revise a document directly inside the chat interface.
Its Agent can now work across an entire project inside the SciSpace Library.
I tested both features as part of the same research workflow. I started with a folder of papers, asked the Agent to compare the findings, and then used that research to revise an actual Word draft.
Setting up a research project in SciSpace
Research projects usually become difficult to manage once the number of papers starts stacking up.
For a literature review, technical report, thesis chapter, or grant proposal, I may need to compare methods, check whether findings agree, trace claims back to the original paper, and look for gaps that could lead to further work.
The sources end up scattered across local folders, browser tabs, reference managers, and previous AI conversations.
Uploading PDFs to a general chatbot like ChatGPT or Claude can help, but it often feels temporary. I may need to upload the same files again in another conversation, explain the project from the beginning, and remind the AI which sources it should use.
SciSpace’s Library Agent lets me organize the papers around the project instead.
For this test, I created a project called “Autonomous Agents topic.”
I added a computer science paper that formalizes memory engineering as the core state-management framework for autonomous AI agents.
SciSpace gives me several ways to build the collection. I can upload PDFs directly, add papers discovered through SciSpace, or bring sources in from Zotero.
Once the papers were inside the project, I could ask the Agent questions about the whole folder. I did not need to open each PDF or attach the same files every time I wanted to try another prompt.
The folder became the working context for the project rather than just another place to store papers.
Asking questions across the entire library
SciSpace returned a structured response titled “Transforming Stateless LLMs into Adaptive Agents.”
The answer explained that memory systems allow language models to persist, organize, and selectively recall information across interactions. Instead of treating every prompt as a fresh session, an agent can carry useful information forward and adjust its behavior based on previous work.
It then broke the topic into several mechanisms, including context compression, retrieval-augmented memory, reflective memory, and hierarchical memory management. The response described memory as a write-manage-read loop, which gave the explanation a clear structure.
I liked that the Agent did not stop at a short paragraph. It turned the answer into a small report with headings, bullet points, and numbered citations. SciSpace also created a file called memory_engineering_adaptive_agents.md, which opened beside the chat.
The chat stayed on the left, while the generated report opened in a larger document view on the right. It felt closer to reading a research note than scrolling through another chat response.
The numbered references were placed beside the relevant claims. I would still open them and compare the explanation with the original paper before using the material in an article or report, but the citations make that checking process easier.
This example only used one paper, although the same project could later include work on memory benchmarks, long-term agent behavior, retrieval systems, and self-improving agents. I could continue asking questions from the same folder without uploading the material again or explaining the topic from the beginning.
Editing a Word document inside the Agent
For the second example, I uploaded the paper DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces and asked SciSpace to create a research document from it.
My prompt was:
Prompt: Create a Word document for a research about a benchmark where data agents produce verifiable tabular results from heterogeneous workspaces
SciSpace used the uploaded paper as the source and generated a structured Word document inside the Agent. The draft included a title, an introduction to the research problem, an explanation of the benchmark, and sections discussing how data agents work across different files, databases, and other data sources.
I liked that the result opened as an editable document beside the chat instead of appearing as another long response. I could read the draft, click into the document, and continue editing it without downloading the file first.
This is useful when I need a starting draft based on a research paper. I can ask the Agent to follow a specific structure, use a more technical tone, or focus on one part of the study. For example, I could ask it to explain how DataSpace verifies tabular outputs or add a section comparing the benchmark with earlier data-agent evaluations.
The next step is where the Word editor becomes more useful. I can ask SciSpace to revise the document it just created:
Tighten the introduction, remove repeated explanations, and make the description of the DataSpace benchmark easier to follow. Preserve the headings and citations, and show all changes as redlines.
SciSpace applies the edits directly inside the document. Additions and deletions remain visible, so I can review the revision before keeping it.
This gives me more control than asking a chatbot to rewrite the section and pasting the result into Word. I can see exactly which sentences changed, whether a technical term was replaced, and whether the Agent removed anything important.
I would still compare the draft with the original paper, especially when it discusses benchmark design, evaluation metrics, and results. The generated document gives me a useful starting point, but the final technical claims still need to be checked against the source.
How researchers and PhD candidates can use it
For researchers and PhD candidates, the Library Agent becomes more useful as a project collects more papers. A thesis chapter, systematic review, grant proposal, or conference paper can have its own folder. The Agent can then answer questions from that material instead of relying on a random mix of uploads.
A researcher could ask it to compare methods, extract definitions, list limitations, or prepare notes for a related-work section. The answers still need verification, but the first pass is easier to organize.
Once the work moves into a draft, a PhD candidate could ask the Word editor to tighten a section, move a paragraph, or revise an unclear explanation while keeping every change visible. The same setup also applies to analysts, technical writers, consultants, and research teams that work from source documents and deliver the final report in Word.
Someone working on a long research project could also create a separate folder for each chapter or workstream. That would keep the source material divided by topic instead of placing hundreds of loosely related PDFs into one collection.
I recommend checking out the various writing tools here.
For collaborative work, the redline editor may be particularly useful. One person can ask the Agent to revise a section, while the rest of the team can review exactly what changed before the document moves forward.
How students can use it
Students could create one SciSpace project for each subject or major assignment. A folder might contain lecture readings, journal articles, course materials, and papers gathered for a final report.
They could compare theories, pull definitions from assigned readings, or prepare a study guide based only on the material in the folder.
After finishing a draft, a student could ask the Word editor for clearer transitions, a shorter introduction, or a more formal tone. The redlines show how the writing changed, so the student still has to review and understand each revision.
It could also help with assignments that require several sources. Instead of discussing each paper in isolation, a student could ask how the authors approach the same question differently or where their conclusions overlap.
The quality of the result will still depend on the papers added to the folder and the questions being asked. Uploading weak sources or using a vague prompt will not suddenly produce a good research paper. The student still needs to choose reliable material, read it, and decide what belongs in the final argument.
How teachers and research supervisors can use it
Teachers could organize papers around one course topic and use the Agent to prepare discussion questions, short comparisons, or an outline of the concepts covered across the readings.
A folder containing the assigned papers for one week could also become a reference workspace. Teachers could ask for connections between the readings, identify terms students may struggle with, or prepare questions that require students to compare the authors rather than repeat a definition.
Research supervisors could use the Word editor while reviewing student drafts. They might ask the Agent to flag repetitive sections, suggest clearer wording, or reorganize part of a literature review. Since the edits appear as redlines, the student can inspect each suggestion.
The tool should not replace academic feedback. A supervisor still needs to judge the argument, evidence, and research quality. It may reduce some repetitive editing and leave more time for comments that require subject knowledge.
There is also a useful teaching opportunity in the redlines themselves. Instead of handing a student a rewritten paragraph, a teacher can use the visible changes to explain why a sentence was shortened, why a claim needed qualification, or why two paragraphs worked better in a different order.
Final thoughts
After using both features together, I can see the workflow SciSpace is trying to build.
I can create a project for one research topic, add the relevant papers, ask questions across the whole collection, and turn the findings into an outline. When I move into writing, I can open the Word document in the same Agent and review the edits through redlines.
That removes several steps from the process and keeps more of the research context in one place.
For researchers who already spend most of their writing time in Word, the redline editor may be the feature that gets the most attention. I think the Library Agent is just as important, though. Without the project folder and its papers, the document editor would only be another rewriting tool.
If you are doing research with AI, I recommend using SciSpace. Let me know what you think in the comments!
SciSpace is also offering 35% off its Max plan for a limited time, including 40,000 credits for running research tasks. Use the code AOAI35 at checkout.
Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!