AI Literature Review and Discovery

Your AI-Powered Literature Search

The days of keyword-only database searches are over. AI literature tools understand meaning, not just words — which means they find conceptually relevant papers, not just keyword matches.

The AI Literature Tool Landscape

Recommended starting workflow: Semantic Scholar for initial discovery → Connected Papers for field mapping → Elicit for structured extraction → Scite for evidence evaluation.

Step 1: Broad Discovery

Start with Semantic Scholar or a general AI tool:

I'm researching [your specific research question].

My field: [discipline]
Key concepts: [list 3-5 core concepts]
Time frame: [e.g., last 5 years, or all time for foundational work]

Find the most relevant papers. Prioritize:
1. Systematic reviews and meta-analyses
2. Highly cited foundational studies
3. Recent empirical work (last 2-3 years)
4. Work from adjacent fields that might offer new perspectives

For each paper, provide: title, authors, year, journal, key finding, and relevance to my question.

✅ Quick Check: Why prioritize systematic reviews and meta-analyses first? Because they synthesize existing evidence — one well-done meta-analysis gives you the conclusions of 20-50 individual studies. Starting with syntheses gives you the landscape before diving into individual papers. It’s the difference between looking at a map before walking every street.

Step 2: Field Mapping

Once you have a set of key papers, use Connected Papers or citation analysis to understand the field’s structure:

What citation networks reveal:

• Clusters — Groups of papers that cite each other heavily (indicating sub-topics or methodological schools)
• Bridges — Papers that connect clusters (often the most innovative work)
• Foundational nodes — Highly cited older papers that established key concepts
• Recent frontier — New papers with rapidly growing citation counts

This structural understanding helps you position your own work: Where does your research question sit in this landscape? Which conversation are you joining?

Step 3: Structured Data Extraction

For systematic reviews or when you need to compare studies:

From these papers about [topic], extract the following data into a table:

For each study, I need:
- Study design (RCT, cohort, case-control, etc.)
- Sample size
- Population characteristics
- Methodology / intervention
- Primary outcome measure
- Key findings (with effect sizes where available)
- Limitations noted by authors

Papers:
[paste titles, DOIs, or abstracts]

Elicit does this natively — upload papers and specify which data points you need. For other AI tools, you can paste abstracts or key sections.

Step 4: Evidence Evaluation

Use Scite or general AI to assess the strength of evidence:

Evaluate the evidence for this claim: [specific research claim]

I need:
1. How many studies support this claim?
2. How many studies contradict or fail to replicate it?
3. What's the methodological quality of the supporting studies?
4. Are there systematic reviews or meta-analyses that address this?
5. What's the overall confidence level: strong, moderate, limited, or contested?

✅ Quick Check: Why is evidence evaluation a separate step from discovery? Because finding relevant papers and assessing their quality are different cognitive tasks. A paper might be highly relevant to your question but methodologically weak. Another might have perfect methodology but study a slightly different population. Separating “what exists” from “what’s trustworthy” prevents you from building your argument on shaky foundations.

Managing Your AI Literature Workflow

Reference management integration: Export AI-found papers directly to Zotero, Mendeley, or EndNote. Most AI tools support export in standard formats (BibTeX, RIS).

Verification protocol: For every AI-surfaced paper you plan to cite:

1. Confirm the paper exists (AI can occasionally hallucinate citations)
2. Read at least the abstract and methods of the original paper
3. Verify that the AI’s summary accurately represents the findings
4. Check the journal’s reputation and the study’s methodology

Key Takeaways

• AI literature tools search by meaning, not just keywords — finding conceptually relevant work across disciplines
• Use a multi-tool workflow: Semantic Scholar for discovery, Connected Papers for field mapping, Elicit for data extraction, Scite for evidence evaluation
• Smart Citations (Scite) distinguish between supporting and contradicting references — far more useful than citation counts alone
• Structured data extraction (Elicit) automates the most time-consuming phase of systematic reviews
• Always verify AI-surfaced papers: confirm existence, read the original, and check that summaries are accurate

Up Next: You’ll learn to use AI for hypothesis generation and research design — turning literature gaps into testable research questions.