How to Conduct a Literature Review Using AI: Live Demo
TL;DR
1-min readIn this hands-on tutorial, Dr. Nellie Deutsch (Ed.D.), an instructor on the MSc in Immersive Technologies, Past Chair of TESOL CALL-IS and founder of Integrating Technology for Active Lifelong Learning, uses real examples to show how AI can support every stage of a literature review. It is designed for researchers, educators and graduate students who want to see AI-assisted review workflows demonstrated rather than described. The tutorial closes with practical guidance on responsible and ethical AI use in research, making clear where checking and interpretation must stay with the researcher.
Key takeaways
- AI can assist across the whole review, from searching for relevant papers and screening them by relevance to summarising content and drafting thematic overviews.
- Live examples on real research questions show how to make sense of complex content and organise insights so they can be used later in the synthesis.
- The tutorial demonstrates how to analyse and compare several papers efficiently by extracting comparable details across studies.
- Validating AI outputs is treated as essential: open each cited paper, confirm the claim appears there with the same meaning, check the reference details and compare summaries with the abstract and results.
- Any statement that cannot be traced back to a source should be treated as unverified until it is confirmed.
- The researcher still defines the question, decides inclusion criteria, judges study quality and writes the final synthesis, since these require subject expertise and accountability.
To apply this in SciSpace, run your question through the Literature Review Generator, compare studies in the Literature Review Matrix, and check each cited paper before writing.
How to do it
Map AI to each review stage
Identify how AI can support each stage of your review, from search to synthesis. This shows you how to conduct a literature review using AI without losing rigour.
Find papers for a real question
Start from a real research question and use SciSpace to find relevant papers. Working on a concrete question keeps the search focused.
Analyse and compare multiple papers
Use Chat with PDF and comparison tools to make sense of complex content and compare several papers efficiently. This speeds up synthesis across studies.
Validate outputs and verify sources
Check every AI output against the sources behind it. Validation keeps your review accurate and defensible.
Use AI responsibly and ethically
Follow responsible and ethical practices when using AI in research, keeping human judgment at the centre. This protects the integrity of your work.
Watch the tutorial28 min
Chapters
- Why use SciSpace Agent for an AI literature review?
- How to start a literature review with SciSpace Agent
- How does SciSpace rank papers by relevance?
- Save, download and run follow-ups like Deep Review
- Turn a literature review into a website or PowerPoint
- How to create an infographic poster of research findings
- Why visualize literature review findings?
- Connect Zotero and Mendeley and find research collaborators
- How to use AI for a literature review ethically
- New to SciSpace? How to use the Agent message box
- Live demo of a literature review website made by SciSpace
- How to search review papers with Citation Explorer
- Can SciSpace make a PowerPoint from your literature review?
Try this on SciSpace
Literature Review Generator
Open agent →Literature Review Matrix
Open agent →Paper Summarizer
Open agent →
Frequently asked questions
How do I check whether AI-generated literature summaries are accurate?
Open each cited paper and confirm that the claim actually appears there, with the same meaning and context. Check that the reference details are correct and the paper exists. Compare the summary with the abstract and results section. Treat any statement you cannot trace to a source as unverified.
Which stages of a literature review can AI support?
AI can help with searching for papers, screening by relevance, summarising content, extracting comparable details across studies and drafting thematic overviews. The researcher should still define the question, decide inclusion criteria, judge study quality and write the final synthesis, since these require subject expertise and accountability.
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