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Guided AI-Driven Literature Reviews with SciSpace

Ilya ShabanovEnglishLiterature reviewStep-by-step AI literature review

TL;DR

1-min read

Ilya Shabanov, creator of EffortlessAcademic.com, presents a guided method for running a literature review with SciSpace, with the emphasis on workflow rather than individual tools. Alongside the practical steps, the tutorial covers the limits and ethics of AI-assisted reviews. It is aimed at students and academics who want a repeatable, AI-supported process instead of a collection of separate tools.

Key takeaways

  • SciSpace Topics helps frame strong research questions that give the review a clear focus.
  • Deep Review supports in-depth exploration of niche scientific subjects.
  • An AI-enhanced notebook is used to store and organise insights as the review develops.
  • Building a growing collection of papers makes it possible to analyse many of them in bulk with AI.
  • To avoid hallucinations and misinformation, rely on tools that link answers to retrievable papers, open and check every cited source, and be cautious with references you cannot find in a database.
  • AI has limitations in literature reviews and raises ethical questions in writing, so keep it for discovery and summarising and base final statements on sources you have read yourself.
Apply it

To apply this, set up a SciSpace notebook and paper collection for your topic, start from a question framed in Topics, and verify every claim before it enters your written review.

How to do it

  1. Frame questions with SciSpace Topics

    Use SciSpace Topics to frame strong research questions. A good question is the foundation of an AI literature review guided.

  2. Explore niche subjects with Deep Review

    Run Deep Review to explore niche scientific subjects in depth. This uncovers literature you might otherwise miss.

  3. Store insights in an AI notebook

    Save and organise your insights in an AI-enhanced notebook. A central record keeps your review coherent.

  4. Build and analyse a paper collection

    Build a growing collection of papers and analyse them in bulk with AI. Bulk analysis saves time on large reviews.

  5. Guard against hallucinations

    Apply strategies to avoid AI hallucinations and misinformation by verifying outputs against sources. Accuracy is essential in a literature review.

  6. Respect ethics and limitations

    Consider the ethical issues and limitations of writing with AI assistance. Responsible use protects the integrity of your work.

Watch the tutorial54 min

Guided AI Literature Review Workflow with SciSpacePlay video: Guided AI Literature Review Workflow with SciSpace

Chapters

  1. Welcome and agenda
  2. How to start an AI literature review without saving 100 papers
  3. How to scope a literature review with SciSpace Find Topics
  4. Save topics to a SciSpace notebook as a literature review outline
  5. Notebook AI: define unfamiliar terms and drill into subtopics
  6. How topic clusters become literature review paragraphs
  7. Deep search vs regular search: when to use each in SciSpace
  8. How SciSpace Deep Review works: follow-up questions and scope
  9. Can you trust AI research answers? Not false, but incomplete
  10. Link Deep Review results to your notebook and shorten them with AI
  11. How to pick which papers to read: recent reviews in high-impact journals
  12. Should you cite preprints from AI search? Double-check citations and impact factor
  13. Bookmark papers into folders and add citations to your notes
  14. Chat with PDF: extract a paper's unique findings
  15. Where are the summaries in a research paper, and when to ask AI instead
  16. How to analyse dozens of papers at once with AI columns
  17. Chat with multiple papers at once: the research oracle
  18. AI literature review workflow in 4 steps: from topics to a written paragraph
  19. Is it ethical to use AI for a literature review? Plagiarism and disclosure
  20. Closing and resources

Try this on SciSpace

Frequently asked questions

How do I avoid AI hallucinations in a literature review?

Use tools that link answers to real, retrievable papers, open and check every cited source, and confirm that the paper actually says what the summary claims. Be cautious with references you cannot find in a database. Keep AI for discovery and summarising, and base your final statements on sources you have read yourself.

What is the difference between a literature review and a systematic review?

A traditional or narrative literature review summarises and interprets research on a topic, and the author has flexibility in which studies to include. A systematic review follows a predefined, documented protocol for searching, screening and appraising studies so the process is reproducible. Both benefit from clear research questions and careful checking of sources.

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