Automate Literature Review with AI: From 20 Papers to a Draft (2026 Workflow)
Writing a literature review is rarely about writing. It’s about filtering, extracting, comparing, and synthesizing across papers.
AI can’t think for you—but it can do the heavy lifting. This post gives a repeatable workflow (Eyesme example) for students and researchers.
The right division of labor
- AI is good at: triage, extraction, structured notes, comparison tables, draft outlines
- You must do: research framing, argument structure, final wording, citation verification
Workflow: from 20 papers to 5 core papers
Step 1: Abstract triage (filter 70–80% fast)
Prompts:
- “Is this paper relevant to {topic}? Score 0–3 and justify in 3 bullets.”
- “Extract: question, method, data, findings, limitations (1–2 lines each).”
Step 2: Build paper cards (structured notes)
Use a consistent card:
- citation (verify manually)
- problem
- method
- data
- results (include numbers if possible)
- limitations
- your notes
Step 3: Cross-paper comparison table
Create a matrix:
- methods
- datasets
- metrics
- key results
- limitations
Step 4: Draft the synthesis (you own the spine)
Prompts:
- “Group these papers into 2–4 method families and name them.”
- “Write a related-work outline (H2/H3) with one topic sentence per paragraph.”
Step 5: Critical analysis
Prompts:
- “What are the biggest validity threats or biases?”
- “What evidence is strongest vs weakest?”
Key warning: citations and numbers must be verified
AI can hallucinate. Never submit invented citations. Use AI for structure and extraction, then verify in the source.
Bottom line
Use AI to turn “too many PDFs” into structured notes and comparisons. Then do the real work: synthesis and judgement.