AI research tools can help you discover papers, read difficult passages, compare studies, and organize evidence. Those are different tasks. A useful research workflow chooses a tool for each stage and keeps the original publication close at hand.
When I did my PhD, much of the work involved searching, reading, annotating, and organizing papers manually. AI can now assist with parts of that work, but deciding what a study means and whether it belongs in a review remains the researcher’s responsibility.
Choose a research tool by the task
- Find studies: Consensus and Elicit support research discovery.
- Explore connections: ResearchRabbit helps follow relationships around relevant papers.
- Understand a passage: Explainpaper assists with difficult sections of a paper.
- Compare evidence: Elicit supports structured research workflows.
- Organize and cite: ReadCube provides literature management and citation tools.
1. Consensus: finding research on a focused question
Consensus accepts research questions and other search formats to find papers. Its search options include browsing papers and AI-assisted synthesis. Use it to locate potentially relevant evidence, then open the sources and check the population, method, and findings. A summary of retrieved papers is not proof that the entire field agrees.
For an education topic, include the learner group and outcome: “What studies examine retrieval practice and delayed retention in secondary science?” This is more useful than asking whether a teaching method is simply “good.”
2. Elicit: searching and organizing evidence
Elicit offers paper search and research workflows for working across studies. It can help structure questions about methods, participants, and results. Our Elicit guide explains how to build a reviewable extraction task.
Define each field before collecting results. “Participants” should distinguish the number recruited from the number analyzed. “Outcome” should name the measure and timing. This makes it easier to spot when an AI answer combines details that belong in separate columns.
3. ResearchRabbit: exploring related literature
ResearchRabbit helps researchers discover related papers and explore literature connections. Start from relevant papers you have already checked, then inspect the recommendations. Citation and similarity relationships help with discovery; they do not establish the quality of a study.
Use several starting papers from different approaches where possible. Beginning with only one author or research tradition can produce a narrow view of the field. Record how you found each additional study so the discovery process remains understandable.
4. Explainpaper: support for difficult reading
Explainpaper lets you highlight confusing text and request an explanation. This may help with unfamiliar terminology or a dense methods section. After reading the explanation, return to the original passage and write your own account of what it says.
Ask a specific question such as “What does this term mean in this study?” rather than requesting an entire paper’s conclusions from a single sentence. Check that a simplified explanation has not removed qualifications or changed the meaning.
5. ReadCube: managing papers and citations
The former Papers website now directs readers to ReadCube, which offers literature management, annotation, citation, and AI-related features. This serves the organizational side of research: keeping papers, notes, and references retrievable. Check current plans for the particular functions you need.
Keep citation metadata separate from your interpretation. Verify authors, year, title, journal, and DOI against the publication before importing a reference into your final bibliography.
A practical workflow for an education literature review
- Define the question, population, setting, and study types you want to examine.
- Search appropriate scholarly databases as well as any AI discovery tool. Save the date and search terms.
- Screen each candidate using explicit inclusion criteria. Log exclusions.
- Retrieve the original papers and record methods, outcomes, and limitations in a consistent evidence matrix.
- Check extracted claims against the source, noting the page or section. Mark unavailable information as missing.
- Write the synthesis around evidence and disagreements, then verify every citation.
A simple evidence matrix
Create columns for: full citation; research question; population and setting; study design; intervention or exposure; comparison; outcome and timing; main finding; limitations; source page; and verification status. Add fields only when they help answer your review question.
For example, two papers may both discuss “AI feedback” while studying different age groups, subjects, or outcomes. Keep those differences visible rather than combining them into a single claim that AI improves learning.
What AI research tools cannot establish for you
A retrieved result is not automatically relevant, a citation is not a guarantee that a claim is supported, and an empty search is not evidence that no studies exist. For a systematic review, follow a documented search and screening protocol and obtain appropriate methodological guidance. Treat vendor performance claims as claims about particular tests, not promises for your own topic.
Use only documents you are entitled to upload, and check institutional rules for unpublished or sensitive research material. Free tiers and paid extraction or export features change; compare the current plans before building a project around a tool.
Choose a detailed workflow: Elicit versus Consensus, a NotebookLM evidence matrix, ResearchRabbit versus Connected Papers, or importing Zotero collections into ResearchRabbit.
Related guides and further reading
Explore more in AI in Education.
- How to use Elicit for a literature review
- NotebookLM for teachers
- Perplexity for research
- AI academic search engines
