SciSpace Academy
Tutorial

How to Plan a Systematic Literature Review Workflow with SciSpace

SciSpace AcademyEnglishSystematic review

TL;DR

1-min read

A systematic literature review succeeds or fails on its planning: before any searching begins, you need a focused question, clear criteria and a defined process for screening, extracting and synthesising evidence. This interactive tutorial, part of the SciSpace live tutorial series, looks at how to plan that workflow and how AI tools such as SciSpace can support it in everyday research. It is designed for graduate students, researchers and faculty preparing to run a systematic review with AI support.

Key takeaways

  • Careful planning comes first, starting with a focused research question that is often framed with a model such as PICO.
  • A written protocol should define inclusion and exclusion criteria, databases, search terms, screening steps, data extraction fields and quality appraisal methods.
  • Registering the protocol where appropriate and following PRISMA reporting guidance helps make the review transparent and reproducible.
  • AI can assist with refining search strategies, screening titles and abstracts, extracting data into structured tables and summarising findings, which are among the most time-consuming steps.
  • Final inclusion decisions, quality appraisal and interpretation should remain with the reviewers, because those choices make a systematic review credible.
  • Documenting how AI was used in the methods section keeps the process open to scrutiny.
Apply it

To apply this, draft your protocol before opening any tool, then use SciSpace for the repetitive search, screening and extraction tasks it defines, reviewing each AI-assisted step yourself.

How to do it

  1. Plan before you search

    Before any searching, plan your systematic literature review workflow in detail. A review succeeds or fails on its planning, so this step protects everything that follows.

  2. Define a focused question and criteria

    Write a focused review question and clear inclusion criteria. These guide every screening and extraction decision you make later.

  3. Map the review stages

    Lay out the main stages from question to screening, extraction and synthesis. A defined process keeps the review consistent and credible.

  4. Use SciSpace for routine tasks

    Bring SciSpace into repetitive review tasks so you can move through them more efficiently. This frees your time for the work that needs expertise.

  5. Keep methodological decisions yours

    Retain control over the methodological decisions while AI assists with routine work. Researcher-led decisions are what make a systematic review credible.

Try this on SciSpace

Frequently asked questions

How do I plan a systematic literature review?

Start with a focused research question, often framed with a model such as PICO. Write a protocol that defines inclusion and exclusion criteria, databases, search terms, screening steps, data extraction fields and quality appraisal methods. Registering the protocol where appropriate and following PRISMA reporting guidance helps make the review transparent and reproducible.

Which steps of a systematic review can AI support?

AI can assist with refining search strategies, screening titles and abstracts, extracting data into structured tables and summarising findings. These tasks are time-consuming, so support can save significant effort. Final inclusion decisions, quality appraisal and interpretation should remain with the reviewers, and AI use should be documented in the methods.

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