SciSpace Academy
Tutorial

Systematic Literature Review from Idea to Execution with AI

Dr. Faheem UllahEnglishSystematic review

TL;DR

1-min read

Dr. Faheem Ullah, Assistant Professor and Cyber Security Program Director at the University of Adelaide, shares the structured workflow he uses for his own systematic literature reviews. The tutorial follows the process end to end and pairs the practical steps with an honest look at what AI can and cannot do. It is aimed at early-career researchers, PhD scholars and faculty who want a more organised, AI-supported approach to literature reviews.

Key takeaways

  • A systematic review follows a documented sequence: a focused question, inclusion and exclusion criteria, searches across databases, screening, data extraction, quality appraisal and synthesis, with guidelines such as PRISMA supporting reproducibility.
  • SciSpace Topics can help identify and frame high-impact research questions at the start of the review.
  • AI can analyse a large set of papers at once, speeding up searching, screening support, summarising and extraction.
  • AI has real strengths in literature work but also clear limits, so it does not replace a transparent, reproducible method.
  • Human reviewers should stay in charge of inclusion decisions and verify AI outputs.
  • Ethical use means documenting exactly how AI was involved and following journal policies on disclosure when AI supports academic writing.
Apply it

To apply this, frame your review question with SciSpace Topics, write down your criteria and search strategy, then use AI to analyse papers in bulk while you check and record every decision.

How to do it

  1. Frame questions with SciSpace Topics

    Use SciSpace Topics to identify and frame high-impact research questions. This is the first stage of a systematic literature review guided.

  2. Follow an end-to-end workflow

    Plan the review as a structured, end-to-end process before collecting papers. A defined workflow keeps the SLR consistent.

  3. Analyse many papers at once

    Use SciSpace's literature review tool to analyse a large set of papers at once. This handles volume that manual reading cannot.

  4. Weigh AI's strengths and limits

    Note where AI does well and where it falls short in your review, and check those areas yourself. Human judgement remains essential.

  5. Address ethical concerns in writing

    Handle the ethical questions that arise when AI supports your academic writing, such as transparency. This keeps the review credible.

Watch the tutorial44 min

Systematic Literature Review with AI: From Idea to ExecutionPlay video: Systematic Literature Review with AI: From Idea to Execution

Chapters

  1. Welcome, agenda and speaker background
  2. How long does a systematic literature review take?
  3. What is a literature review and why should you do one?
  4. Types of literature reviews: what is a systematic literature review?
  5. How to write a literature review: the 8 systematic review steps
  6. How to choose a review topic and write research questions
  7. How to build a search string and check it with a pilot study
  8. Inclusion and exclusion criteria for a systematic review
  9. Screening 4,634 papers down to 74: the selection process
  10. Data extraction form and thematic analysis in a systematic review
  11. How to find papers for a literature review with SciSpace AI
  12. Automate systematic review data extraction with SciSpace
  13. How to paraphrase academic writing with the SciSpace Chrome extension
  14. Generate an APA citation with the SciSpace citation generator
  15. How will AI change academic research?
  16. Using AI in research without plagiarism: what AI should and shouldn't do
  17. What skills do researchers need in the age of AI?

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Frequently asked questions

What are the main steps of a systematic literature review?

A systematic review typically defines a focused research question, sets inclusion and exclusion criteria, searches multiple databases with a documented strategy, screens titles, abstracts and full texts, extracts data, appraises study quality and synthesises the findings. Reporting guidelines such as PRISMA help document each step so that others can reproduce the review.

Can AI be used in a systematic literature review?

Yes. AI can speed up searching, support screening, summarise studies and assist with data extraction, but it does not replace a transparent, reproducible method. Researchers should verify AI outputs, keep human reviewers in charge of inclusion decisions, document exactly how AI was used and follow journal policies on disclosure.

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