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Agentic AI for Researchers: From Literature Search to Submission

Dr. Ali HindiEnglishLiterature reviewEnd-to-end research workflow with AI agents

TL;DR

1-min read

This researcher-led tutorial shows how agentic AI can support a research project guided, starting with the first literature search and ending with a manuscript ready for submission. It is aimed at researchers and graduate students who want a structured, practical way to bring AI agents into their work without handing over control of the research.

Key takeaways

  • An agentic workflow differs from a chatbot because it links tasks such as searching, screening, reading, synthesising and drafting, instead of answering one isolated prompt at a time.
  • SciSpace can serve as a single place to organise the whole project, from the initial search through to journal submission.
  • Human checkpoints should be built in at every stage, so the researcher reviews outputs and makes the decisions that shape the project.
  • The path from search results to synthesis and then to a structured draft is easier to manage when each step builds on the one before it.
  • AI can shorten repetitive work, but framing the research question, judging the evidence and owning the final manuscript remain the researcher's responsibility.
  • Using an agent well should free time for expert tasks such as appraising evidence, interpreting results and building the argument, not replace that thinking.
Apply it

To apply this, map your current project onto the search, synthesis and drafting stages in SciSpace, and decide in advance where you will pause to review the agent's output before moving on.

How to do it

  1. Map your project onto workflow stages

    List your current project's search, synthesis and drafting stages. Mapping the work this way shows where agentic AI for researchers can link tasks instead of answering isolated prompts.

  2. Start with a literature search in SciSpace

    Begin the project in SciSpace with your first literature search, so the whole workflow through to journal submission is organised in one place.

  3. Set human checkpoints in advance

    Decide before you start where you will pause to review the agent's output. Checkpoints keep you in control of the decisions that shape the project.

  4. Move from results to synthesis

    Let each step build on the previous one, turning reviewed search results into a synthesis. Building stepwise makes the path to a draft easier to manage.

  5. Build a structured manuscript draft

    Turn the synthesis into a structured draft, then spend the saved time appraising evidence, interpreting results and building the argument yourself.

Watch the tutorial36 min

Agentic AI for Researchers: From Literature Search to SubmissionPlay video: Agentic AI for Researchers: From Literature Search to Submission

Chapters

  1. Welcome: which literature review pain points can AI solve?
  2. What does an AI-assisted literature review workflow look like?
  3. Why start with a good research question?
  4. Quick search vs Deep Research vs PRISMA systematic search
  5. How does PRISMA mode build PICO questions and criteria?
  6. How do you get reproducible search strings for Google Scholar and PubMed?
  7. How do you build a research library and open paywalled papers?
  8. How do you analyse and synthesise many papers at once?
  9. One prompt for conclusions, disagreements and research gaps
  10. How can you use AI ethically when writing a literature review?
  11. Running a Deep Review report: keywords, dates and filters
  12. How does a verified report check its own citations?
  13. Turning your review into figures and tables for posters
  14. How do you find the right journal for your review?
  15. How should you disclose AI use? NICE and Cochrane guidance

Try this on SciSpace

Frequently asked questions

What is agentic AI in research?

Agentic AI refers to AI systems that can plan and carry out a sequence of tasks toward a goal, such as finding papers, extracting key details and drafting text, rather than answering one prompt at a time. In research, the researcher still defines the question, reviews each output and decides what goes into the final work.

Does using an AI agent mean the researcher does less thinking?

It should not. A well-designed workflow hands repetitive steps such as searching and summarising to the agent, freeing time for the parts that need expertise: choosing the question, appraising evidence, interpreting results and writing the argument. Researchers remain accountable for every claim and citation in the work they submit.

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