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Beyond Google Scholar: Using Agentic AI for Literature Search

SciSpace AcademyEnglishLiterature reviewFind research papers with AI

TL;DR

1-min read

Keyword searches in Google Scholar return long lists but leave researchers to judge relevance, follow citation trails and extract the details that matter. This tutorial looks at how agentic AI changes that process, using SciSpace to illustrate the approach. It is intended for students and researchers who rely on Google Scholar and want a more efficient, AI-assisted way to find and organise relevant papers.

Key takeaways

  • Traditional keyword search leaves gaps, because matching terms does not tell you which papers actually address your question.
  • An AI agent can turn a single research question into several targeted searches and gather candidate papers from them.
  • AI can summarise those candidate papers and organise their key information, so less time goes into sifting and more into evaluating.
  • Google Scholar and AI search tools serve different purposes, and many researchers combine the broad index with AI-assisted screening and summarising.
  • Every paper found by AI needs to be checked against the original before it is included in a review.
  • The researcher still sets the question and inclusion criteria and makes the final decisions about what to include.
Apply it

To apply this, enter your research question in SciSpace, compare the papers the agent returns with your usual Google Scholar results, and verify each candidate in its original source before adding it to your review.

How to do it

  1. Enter your research question in SciSpace

    Type your research question into SciSpace rather than a string of keywords. Matching terms alone does not tell you which papers actually address your question.

  2. Let the agent run targeted searches

    Use the agent to turn that one question into several targeted searches and gather candidate papers. This is the core of agentic AI literature search.

  3. Review summaries of candidate papers

    Read the AI summaries and organised key information for each candidate paper. Less time sifting leaves more time for evaluating relevance.

  4. Compare with your Google Scholar results

    Compare the agent's papers with your usual Google Scholar results. The two serve different purposes, and combining them can close gaps.

  5. Verify papers before including them

    Check each candidate against its original source and apply your own inclusion criteria before adding it to your review.

Try this on SciSpace

Frequently asked questions

Is AI literature search better than Google Scholar?

They serve different purposes. Google Scholar is a broad index that relies mainly on keyword matching, while AI search tools can interpret a question in natural language, summarise results and help organise them. Many researchers use both, then verify every paper's relevance and details directly from the source before citing it.

What is agentic AI in literature search?

It describes an AI system that carries out several linked steps toward a goal, such as reformulating a research question, running multiple searches, screening results and summarising findings, rather than answering a single prompt. The researcher still sets the question and inclusion criteria and reviews everything the agent finds.

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