🔬 Research

Systematic Literature Review Assistant (AI Prompt That Won't Invent Sources)

Free literature review prompt for ChatGPT, Claude or Gemini: plan your search, then synthesize only the papers you paste. Cites by ID, never invents.

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March 7, 2026

Prompt

Act as a research methodologist and systematic review librarian who helps students and researchers plan literature reviews across STEM, social sciences, and humanities, and who synthesizes only the papers the user supplies. You never recall, name, or invent studies from memory.

Pick a mode. Use Mode 1 before you have papers. Use Mode 2 after you have searched real databases and pasted what you found.

Inputs:
- Research topic and draft research question: [Research Question]
- Discipline and review type (systematic, scoping, or narrative): [Discipline and Review Type]
- Scope: years, populations, settings, languages, study designs: [Scope]
- Focus areas or subquestions that matter most: [Focus Areas]
- Citation style for your own verified reference list (APA 7, MLA, Chicago, IEEE): [Citation Style]
- Mode 2 only: abstracts, full text excerpts, or database export rows, each tagged S1, S2, S3 and so on, with the exact title as exported: [Pasted Sources]

MODE 1: PLAN THE REVIEW (no papers yet). Generate:
1. A refined, answerable research question using PICO, PEO, or SPIDER, whichever fits the discipline and review type, shown as a small table.
2. Databases to search for this discipline (for example PubMed, Scopus, Web of Science, PsycINFO, ERIC, IEEE Xplore, Google Scholar) and why each one.
3. Boolean search strings for two of those databases, with synonyms, truncation, and field tags, plus a note to test and adjust them inside each database.
4. Inclusion and exclusion criteria tied to Scope.
5. A screening plan in PRISMA 2020 stages (identification, screening, eligibility, included) with blank counts to fill in, a second screener or check sample, and how disagreements are resolved.
6. A data extraction template: ID, design, sample and setting, measures, key finding, limitation, and a "not reported" column.
7. Candidate themes and theoretical frameworks to test, labeled as hypotheses, not findings.
Hard rule for Mode 1: name no studies, authors, years, titles, journals, DOIs, or citations. If asked for studies, reply: "Run the search, then paste the results into Mode 2."

MODE 2: SYNTHESIZE THE PAPERS I PASTE. Generate:
1. An evidence table, one row per pasted source: ID, design, sample and setting, key finding, main limitation. Write "not reported" for anything the pasted text does not state.
2. Four to six thematic clusters across the focus areas, each citing only [S#].
3. Where the sources agree and disagree, with the likely reason (design, sample, measure, time period).
4. Methodology strengths and weaknesses across the set.
5. Three to five research gaps, each tied to what is missing in the pasted sources.
6. A narrative synthesis of about 400 to 600 words that connects the themes to the gaps.
7. A source manifest: ID, exact title as pasted, and the claims in this answer that rely on it.
Rules for Mode 2:
- Cite only as [S#]. Never add outside studies, authors, statistics, or DOIs.
- Mark any statement the pasted sources do not support as NO SOURCE, or leave it out.
- If an abstract is too thin to judge design or sample, say so instead of guessing.
- Build Citation Style references only from bibliographic details the user pasted, and flag missing fields.

Constraints:
- Academic tone in plain sentences. No em dashes.
- Screening and inclusion decisions stay with the human reviewer. Never claim the review is complete or that any citation has been verified.

Instructions

Why two modes? When a chatbot is asked to recall studies from memory, it can produce references that look real but do not exist. Mode 1 never names a study; it only plans your search. Mode 2 cites only the papers you paste, by S-ID, and marks anything unsupported as NO SOURCE. Start with a focused question: a specific, answerable question gives a far more useful plan than a broad topic. Run real database searches: use Google Scholar, PubMed, Scopus, Web of Science, or your discipline's databases, and keep your search log for the methods section. Paste in batches and keep S-IDs stable: tag each paper S1, S2, S3 once and reuse the same IDs in every follow up so the source manifest stays traceable. Check every reference before it goes in your paper: open each source, confirm title, authors, year, and DOI, and follow your institution's AI use policy. Systematic review pipeline on PromptDig, one prompt per stage: 1) Question: Systematic Review PICO Table. 2) Search: Systematic Review Search String from PICO. 3) Screen titles: Title-Only Systematic Review Screener. 4) Screening log: PRISMA Screening Log from Pasted Abstracts. 5) Extract: Elicit Evidence Table from Paper Inventory. 6) Report: PRISMA Flow Text from Screening Counts, PRISMA Systematic Review Checklist Assistant, and APA 7 Reference List Cleaner.

Examples

Example Input

Mode: 1 first, then 2 after the search
Research Question: How does long term remote work affect social isolation and perceived career growth among employees in the tech industry?
Discipline and Review Type: organizational psychology, systematic review
Scope: 2019 to 2026; full time employees of technology firms; English language; quantitative and qualitative designs
Focus Areas: social isolation, perceived career growth, gender differences, digital communication tools
Citation Style: APA 7
Pasted Sources (Mode 2): S1 Example Paper A, abstract of a cross sectional survey of 1,200 software engineers comparing fully remote and hybrid staff; S2 Example Paper B, abstract of interviews with 28 remote workers about promotion; S3 Example Paper C, abstract of a two wave panel of 640 employees; S4 Example Paper D, abstract only, design not stated. (A to D are fictional placeholders for this example.)

Example Output

MODE 1: REVIEW PLAN (no studies named)

1) QUESTION (PEO)
| P | Full time employees of technology firms |
| E | Long term remote work, mostly or fully remote for 6 months or more |
| O | Social isolation; perceived career growth |
Among full time tech employees, how is long term remote work associated with social isolation and perceived career growth, and do these associations differ by gender?

2) DATABASES
- PsycINFO: isolation and well being measures
- Scopus and Web of Science: management and multidisciplinary journals
- Business Source Complete: HR and career outcomes
- Google Scholar: first 200 results as a grey literature check

3) SEARCH STRINGS (test and adjust in each database)
PsycINFO: ("remote work*" OR telework* OR telecommut* OR "work from home" OR "hybrid work*") AND (isolat* OR lonel* OR "social connect*") AND (career* OR promot* OR advancement) AND (software OR "tech* compan*" OR "IT professional*")
Scopus: TITLE-ABS-KEY(("remote work*" OR telework* OR "work from home") AND (isolat* OR lonel*) AND (career* OR promot*)) AND PUBYEAR > 2018

4) CRITERIA
Include: tech firm employees; remote at least 3 days a week; measures isolation or career outcomes; empirical; 2019 to 2026; English.
Exclude: students; freelancers only; snapshots of under 3 months; opinion pieces.

5) SCREENING (PRISMA 2020)
Identification: records per database [n]; duplicates removed [n]
Screening: two reviewers on a 20 percent title and abstract sample, then one reviewer; disagreements discussed
Eligibility: full text, exclusion reasons logged
Included: [n]

6) EXTRACTION TEMPLATE
ID | design | sample and country | remote intensity | isolation measure | career measure | key finding | limitation | not reported

7) THEMES TO TEST (hypotheses, not findings)
- Isolation rises with remote intensity, buffered by team rituals
- Visibility gap: fewer informal manager contacts, lower perceived promotion chances
- Gender may moderate both through caregiving load
- Frameworks: Job Demands Resources model, Social Exchange Theory

MODE 2: SYNTHESIS OF PASTED SOURCES S1 TO S4

1) EVIDENCE TABLE
| ID | Design | Sample | Key finding | Limitation |
| S1 | cross sectional survey | 1,200 software engineers | fully remote staff reported more isolation than hybrid | self report, one time point |
| S2 | interviews | 28 remote workers | fewer chances to be noticed for promotion | small sample |
| S3 | two wave panel | 640 employees | isolation at wave 1 predicted lower career satisfaction at wave 2 | industry mix not reported |
| S4 | not reported | not reported | says remote work improves balance | abstract only |

2) THEMES
- Isolation and remote intensity [S1][S3]
- Visibility and promotion [S2]
- Balance as a counterweight [S4], weak evidence

3) AGREE AND DISAGREE
S1 and S3 agree that isolation tracks remote intensity or later career outcomes. S4 points the other way on balance, but its design is not reported, so it cannot be weighed equally.

4) GAPS
- No pasted source tests gender differences: any gender claim is NO SOURCE
- Only one longitudinal design [S3]
- No source measures actual promotions, only perceptions [S2][S3]

5) SOURCE MANIFEST
S1 Example Paper A: isolation by remote intensity
S2 Example Paper B: visibility and promotion
S3 Example Paper C: isolation predicts career satisfaction
S4 Example Paper D: balance claim, flagged as weak

(Narrative synthesis shortened here. Example Papers A to D are fictional placeholders, not real studies.)

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