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TutorialLiterature review › Step-by-step AI literature review

Guide to a Literature Review Using AI Agents

Dr. Lyndon WalkerEnglishLiterature reviewStep-by-step AI literature review

TL;DR

1-min read

A high-quality literature review is one of the most demanding and most important parts of research. In this tutorial, Dr. Lyndon Walker, a PhD-qualified statistician, Senior Education Researcher at the Royal Australian College of General Practitioners and content creator on AI, statistics and research workflows, walks through a practical workflow for using AI agents in a review. It is aimed at researchers and graduate students who want to speed up literature reviews without lowering rigour. The approach shows how to gain speed from agents while keeping the integrity that a credible review requires.

Key takeaways

  • Unlike a chatbot that answers one prompt at a time, an agent can plan and carry out a series of steps, such as searching, filtering and summarising many papers toward a goal.
  • The tutorial shows how to structure a review workflow around an agent so that each stage has a clear purpose.
  • Agents make it possible to search and analyse a large number of papers efficiently, which is where much of the time saving comes from.
  • AI can then be used to summarise, compare and synthesise findings across studies to build the body of the review.
  • Checking outputs is a central focus: require citations for every claim, open the cited papers, confirm the statements, check reference details and spot-check numbers against results sections.
  • Anything that cannot be traced to a real source should be removed or confirmed before it enters the review, and ethical best practices close the tutorial.
Apply it

To apply this in SciSpace, start with the Literature Review Generator, organise studies in the Literature Review Matrix and verify each cited claim against the source paper.

How to do it

  1. Structure your review around an agent

    Plan your literature review using AI agents by deciding which steps SciSpace Agent will handle. A clear structure keeps the workflow efficient.

  2. Search and analyse many papers

    Use the agent to search and analyse a large number of papers efficiently. This lets you cover more literature in less time.

  3. Summarise, compare and synthesise

    Ask the agent to summarise papers, compare them and synthesise their findings. This turns individual papers into a coherent picture of the field.

  4. Check outputs against sources

    Verify each AI-generated claim against the original source. Grounding claims in sources keeps your review trustworthy.

  5. Follow ethical best practices

    Apply ethical best practices for AI use throughout your academic research. Transparent use protects your credibility.

Watch the tutorial36 min

Literature Review with AI Agents: A WorkflowPlay video: Literature Review with AI Agents: A Workflow

Chapters

  1. How deep should a literature review be? PhD vs journal article vs report
  2. Can AI help write your literature review? What AI should and shouldn't do
  3. What is SciSpace? Tour of the AI research tools and agent gallery
  4. SciSpace literature review: Standard mode and Find Topics for research gaps
  5. High Quality literature review: paper table with custom columns
  6. What is Deep Review? AI agent screens 1,750 papers into one literature review
  7. How to read paywalled papers with LibKey or filter to open access
  8. How to check AI literature review citations are real using the DOI
  9. Chat with PDF in SciSpace: explain text, equations and graphs
  10. How to use SciSpace Agent to turn a literature review into slides or an infographic
  11. Chain AI agents: deep review to grant search to board presentation
  12. Can you connect Zotero or Mendeley to SciSpace?
  13. Are AI detectors reliable? AI policies and research integrity
  14. Verifying AI output, privacy and echo-chamber bias in research
  15. How to declare AI use like SciSpace or ChatGPT in a journal article

Try this on SciSpace

Frequently asked questions

What is the difference between an AI agent and an AI chatbot for research?

A chatbot answers one prompt at a time, while an agent can plan and carry out a series of steps, such as searching, filtering and summarising many papers toward a goal. Agents can save more time on multi-step tasks, but their outputs still need the same careful verification.

How do I make sure AI-generated literature summaries are grounded in sources?

Require citations for every claim, open the cited papers and confirm the statement appears there, and check reference details. Spot-check numbers and conclusions against the results sections. Anything you cannot trace back to a real source should be removed or confirmed before it goes into your review.

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