AI Automation for Researchers

AI Automation for Researchers

AI automation can help researchers reduce repetitive information work without handing over responsibility for evidence, interpretation, or conclusions.

A research workflow may:

• organise collected sources;
• extract defined fields from papers;
• summarise documents;
• classify material by topic;
• prepare evidence tables;
• process interview or survey text;
• identify missing information;
• compare findings;
• create recurring research briefs; or
• prepare a first draft for review.

The strongest use cases are structured and reviewable.

AI should help researchers find, organise, and transform information. It should not be treated as an authority that can confirm whether a claim is true merely because it produces a fluent answer.

A practical approach is:

1. define the research task;
2. preserve the original sources;
3. ask the model for a structured result;
4. separate evidence from inference;
5. validate important claims;
6. record uncertainty; and
7. keep the researcher responsible for the final interpretation.

What research work can be automated?

AI automation is useful for repeated tasks that involve language or documents but still produce an output that can be checked.

Suitable examples include:

• screening titles and abstracts against stated criteria;
• extracting authors, dates, methods, populations, and findings;
• grouping sources by topic;
• summarising individual papers;
• comparing several documents;
• coding interview or survey responses;
• turning notes into structured records;
• preparing literature-monitoring updates;
• identifying unanswered questions; and
• formatting research material consistently.

Some research tasks should remain human-led.

These include:

• deciding whether evidence is credible;
• interpreting ambiguous findings;
• assessing methodological quality;
• choosing between competing explanations;
• determining whether evidence supports a conclusion;
• making ethical decisions; and
• approving material for publication.

AI can prepare information for these decisions, but it cannot assume the researcher's accountability.

Start with one narrow research task

Avoid trying to automate an entire research project.

Choose one repeated task with a clear input and output.

Instead of:

Automate the literature review.

choose:

Read each abstract and return the stated population, intervention,
outcome, study type, and whether the abstract appears to match the
predefined inclusion criteria.

This narrower task is easier to test.

The researcher can compare every extracted field and screening suggestion with the source.

A good first task is:

• frequent;
• time-consuming;
• low risk;
• based on available source material;
• expected to produce a defined format; and
• easy to review.

Literature discovery and triage

AI can support the early stages of a literature search by organising results returned from approved databases or search tools.

A workflow may:

1. receive titles, abstracts, and source details;
2. remove obvious duplicates;
3. extract relevant concepts;
4. classify items against stated criteria;
5. group them by topic; and
6. return uncertain items for review.

The search strategy itself should remain visible.

Record:

• databases or sources searched;
• search terms;
• filters;
• search dates;
• inclusion and exclusion criteria; and
• reasons for removing items.

AI classification should not silently replace the researcher's screening process.

Include an Unclear or Human review option rather than forcing every item into Include or Exclude.

Extracting structured information from papers

AI can convert varied papers into a consistent evidence table.

Fields may include:

• citation;
• publication date;
• research question;
• study design;
• sample or population;
• setting;
• methods;
• intervention or exposure;
• outcomes;
• main findings;
• reported limitations;
• funding; and
• source location.

Ask for exact fields and explain how missing information should be handled.

For example:

Extract the requested fields from the paper.

If a field is not stated, write "Not reported."
Do not infer a method, sample size, or conclusion.
Include the section or page supporting each important field.

Structured output makes comparison easier, but it does not guarantee that the extracted information is correct.

Verify names, numbers, methods, results, and source references.

Summarising research documents

A general summary may hide the details a researcher needs.

Define the summary structure.

For a paper, this may include:

• research question;
• methods;
• sample;
• main findings;
• limitations;
• author conclusions;
• unanswered questions; and
• source references.

Keep the author's conclusions separate from the model's interpretation.

A useful output may contain:

Findings stated by the authors:
Limitations stated by the [authors:
Additional limitations identified](/ai-automation/benefits-and-limitations-of-ai-automation) for review:
Questions requiring researcher judgement:

The last two sections are not source facts.

Label them clearly.

Important research decisions should still be based on the complete paper, not only the generated summary.

Handling long papers and reports

Long documents may need to be processed in stages.

A workflow can:

1. divide the source by meaningful section;
2. summarise each section using the same fields;
3. preserve section or page references;
4. combine the partial results;
5. identify duplicate or conflicting statements; and
6. prepare a final structured summary.

Divide by headings where possible.

A split in the middle of a table, method, or argument can remove important context.

The combining step should not introduce claims that were absent from the source or partial summaries.

Keep the original document available for review.

Building evidence tables

Evidence tables make findings across sources easier to compare.

An AI-assisted workflow may create one row per study with columns such as:

Use consistent field definitions.

For example, decide whether Main result should contain:

• the authors' exact result;
• a short paraphrase;
• a numerical outcome;
• statistical information; or
• the researcher's interpretation.

Do not mix these without labels.

Require source references for important entries.

Review every row before using the table for synthesis or publication.

Comparing sources

AI can help organise similarities and differences across papers or reports.

Define the comparison criteria before supplying the sources.

For example:

Compare the studies by:
1. research question;
2. design;
3. population;
4. measurement method;
5. main finding;
6. limitation; and
7. result that cannot be compared directly.

Ask the model to preserve disagreement.

A comparison should not force conflicting studies into one conclusion.

It should make differences in definitions, methods, populations, time periods, and evidence quality visible.

The researcher remains responsible for deciding whether sources are comparable.

Interview and qualitative research workflows

AI can support qualitative research by organising transcripts or notes.

Possible tasks include:

• removing repeated formatting;
• creating speaker-labelled summaries;
• extracting quotations related to a defined question;
• applying an initial coding framework;
• grouping codes into candidate themes;
• identifying contradictory responses;
• creating case summaries; and
• listing material requiring manual review.

Automated coding should be treated as a proposal.

It may miss context, irony, emotion, cultural meaning, or contradictions.

Preserve the original transcript and source location for every quotation.

Do not allow the model to create quotations or merge the words of different participants.

When the material is sensitive, review consent, access, provider, storage, and retention requirements before processing it.

Survey and free-text response processing

AI can classify and summarise large collections of free-text responses.

A workflow may:

• assign responses to approved categories;
• identify recurring topics;
• extract stated concerns;
• separate positive and negative feedback;
• flag responses needing urgent attention; and
• prepare a thematic overview.

Define the categories and include an Other or Unclear option.

Test examples that could fit several categories.

Do not use a general sentiment label as a complete interpretation of a participant's meaning.

Preserve enough source detail for the researcher to review unusual or important responses.

Recurring research briefs

A recurring workflow can prepare updates from newly collected material.

For example:

New Sources
→ Extract Metadata
→ Classify by Topic
→ Summarise New Findings
→ Compare With Earlier Brief
→ Return Draft for Review

The brief should identify:

• the period covered;
• sources included;
• new findings;
• changes from the previous period;
• conflicting evidence;
• missing information; and
• questions for further investigation.

Run the workflow manually before scheduling it.

Confirm that failed searches, unavailable sources, duplicates, and empty result sets remain visible.

Research administration

Not every useful automation needs to analyse evidence.

Researchers can also automate preparation and administration, including:

• turning meeting notes into action items;
• formatting project updates;
• preparing participant communication drafts;
• organising file descriptions;
• creating handover notes;
• checking whether required document sections are present;
• drafting data-management checklists; and
• preparing recurring progress reports.

These tasks may be easier to evaluate than research interpretation.

They can provide a practical starting point for a team learning how AI workflows behave.

Preserve provenance

Provenance explains where information came from and how it moved through the workflow.

Record:

• source title;
• author or organisation;
• publication date;
• link or identifier;
• access date where relevant;
• document version;
• page or section;
• search method;
• model and provider;
• workflow version; and
• reviewer corrections.

A generated summary without its source is difficult to verify.

When the workflow combines documents, preserve source attribution for each important claim.

Do not cite a model-generated sentence as though it were the original evidence.

Separate evidence from inference

A research workflow should distinguish between:

• facts stated in a source;
• author interpretations;
• model-generated summaries;
• model-generated inferences;
• researcher interpretations; and
• unresolved questions.

Use separate fields or headings.

For example:

Evidence from sources:
Author conclusions:
AI-proposed interpretation:
Researcher review:
Remaining uncertainty:

This reduces the risk that a plausible model suggestion becomes part of the evidence record.

Reduce hallucinations

Ground the model in the supplied sources.

Use instructions such as:

Use only the provided documents.
If the answer is not stated, write "Not reported."
Do not invent citations, quotations, page numbers, methods, or results.

Require source references where practical.

Validate that the cited section exists and supports the claim.

Use fixed checks for required fields and allowed values.

A second model can help identify unsupported statements, but it is not an independent guarantee.

Human source review remains necessary for important findings.

Protect sensitive research data

Research data may contain confidential, personal, proprietary, or unpublished information.

Before using a workflow, identify:

• which model receives the data;
• whether it is local or cloud-based;
• which tools receive it;
• where source and output files are stored;
• what appears in logs;
• who can access the result;
• whether consent permits the proposed use; and
• how long information is retained.

Remove unnecessary identifiers.

Use synthetic or non-sensitive material during early testing.

A local model can keep model processing on the researcher's computer, but the process is only fully local when its tools, sources, and destinations also remain local.

Human review and research integrity

Human review is necessary when the output contributes to:

• inclusion or exclusion decisions;
• evidence assessment;
• qualitative interpretation;
• methodological conclusions;
• participant communication;
• public claims;
• publication; or
• high-impact recommendations.

Reviewers need the original source, generated result, missing information, and any tool activity.

AI use should be documented according to the requirements of the research organisation, funder, publisher, ethics process, or discipline.

Do not describe an automated result as independently verified unless it was actually checked.

Build a research workflow in Feluda

Feluda is a desktop application for testing and building visual AI workflows.

Begin in Workbench.

Test one instruction with representative, non-sensitive source material.

Compare the model output with the source and refine the structure.

In Studio, use focused blocks:

• LLM for summaries, comparisons, and draft briefs;
• LLM Label for topic or screening categories;
• LLM Extract for metadata and evidence fields;
• Expression for required-field checks and fixed conditions;
• Emit for useful intermediate results; and
• Output for reviewable findings or clear errors.

A simple evidence workflow may look like:

Paper Text
→ LLM Extract Study Fields
→ Expression Check Required Fields
→ LLM Create Structured Summary
→ Output for Researcher Review

Feluda can connect to supported cloud providers and compatible local models.

Choose the model according to source length, instruction following, privacy, required media support, speed, and available hardware.

Use tools and Genes carefully

Genes can add tools, prompts, flows, and resources.

A research tool may retrieve current information, search an approved source, process a file, or save a result.

Before using it, check:

• the source it accesses;
• the query or information it receives;
• whether it is current enough for the task;
• whether it reads or writes;
• whether it uses an external service; and
• how the returned result can be verified.

Review tool activity rather than relying only on the model's description.

Confirm that saved records and files exist at the intended destination.

Test the workflow

Use RunFlows with a representative test set.

Include:

• a normal source;
• a short source;
• a long source;
• missing fields;
• conflicting findings;
• an irrelevant source;
• an unreadable or unsupported file;
• every classification route;
• an unavailable model; and
• a tool failure.

Compare outputs with expected results.

Track invented facts, omitted facts, incorrect fields, invalid references, wrong routes, and required corrections.

Re-run the test set after changing the model, prompt, source format, tool, or workflow logic.

Measure whether the workflow helps

Measure:

• time saved;
• extraction accuracy;
• screening agreement;
• unsupported claims;
• reviewer correction time;
• completion and failure rates;
• cost per approved result;
• source coverage;
• tool reliability; and
• researcher satisfaction.

Do not measure success only by the number of processed documents.

A workflow is useful when it improves the research process while preserving evidence quality, provenance, and appropriate human judgement.

Start with research assistance, not autonomous conclusions

Choose one repeated, reviewable task.

Preserve the source.

Define a structured output.

Keep missing information visible.

Separate source evidence from AI inference.

Test difficult examples and record corrections.

Use AI to reduce repeated preparation and organisation, while researchers retain responsibility for methods, interpretation, ethics, and conclusions.