Elicit

What is Elicit?

When preparing a literature review, finding papers is just the beginning. Next, you need to screen studies, compare methods, extract data, and verify the sources of each conclusion. Elicit provides AI-assisted support for these tasks, linking research questions, paper sources, and structured results. It is ideal for graduate students, researchers, and professional users who need to conduct analyses based on academic evidence.

It supports semantic search, so it does not have to rely entirely on exact keywords; it can also organize relevant materials into reports and tables. Researchers can adjust the scope of the papers covered and the information extracted, gradually narrowing a broad question down to a list of verifiable evidence.

Key Features

1. Academic Literature Search:Search for relevant papers and research materials on natural language topics to help identify literature that discusses the same issue using different formulations.
2. Filtering and Data Extraction:Assist with the screening, data extraction, and tabulation of information in a systematic literature review to facilitate the comparison of samples, methods, results, and other information.
3. Research report from the source:Organize the evidence into readable research material, retain citation references, and allow for adjustments to the scope of the research covered in the report.
4. Databases and Routines:Save research sources and set up research tasks to be performed daily or weekly, so that new evidence can be incorporated into the subsequent update process.

Core Value: Ensuring that conclusions are grounded in evidence

The practical value of Elicit lies in integrating searching, organizing, and verifying into an interconnected workflow. When faced with dozens of papers discussing similar topics, you can first establish consistent extraction fields, then identify the commonalities and differences among the studies, thereby reducing the need to switch back and forth between documents, notes, and tables.

For topics that researchers follow over the long term, Routines can repeatedly search for new relevant evidence, update materials, and provide summaries of changes. This is useful for maintaining research briefings or tracking rapidly evolving topics; however, researchers must still evaluate the significance of new papers, the quality of the research, and whether they alter their original conclusions. Summaries generated by AI cannot replace reading the full text, and correlation does not imply causation.

How to Use: Start with Questions You Can Answer

1. Specific Questions:Describe the study population, intervention or variables, outcomes of interest, and time frame; avoid proposing a topic that is too broad.
2. Check the search scope:Review the titles, abstracts, and sources of the references, add keywords and exclusion criteria, and confirm that there are no deviations from the research question.
3. Design Extract Fields:Compare and analyze the study design, sample size, key findings, and limitations as required by the assignment, and cross-check each item against the original text.
4. Compiled and continuously updated:Create a table or report documenting the sources; when tracking is required, configure the subject, frequency, and return format for Routines.

It is safer to first validate the results using a small-scale project you are familiar with. Report, extraction, and automated task quotas may vary by plan; please refer to the current product page for details. When dealing with unpublished research or sensitive materials, you should also review the data usage terms first.

How do I choose among similar tools?

If the deliverable is a traceable document evidence table, you should prioritize learning about Elicit; if the goal is to memorize and understand learning materials, Heuristica aligns more closely with the learning process; and if you need to integrate search functionality into your own software, you should focus on Exa’s development interface.