AI Tools for Scientists
For grad students, postdocs & faculty
AI can help you discover literature, synthesize evidence, and work faster—without replacing your judgment. This page highlights four research-focused tools and how to use them responsibly.
Why use AI in research?
- Keep up with the literature — Semantic search and summarization help you scan more papers without drowning in full text.
- Synthesize across studies — Tools that extract claims and methods make systematic reviews and meta-analyses more tractable.
- Save time on discovery and note-taking — Focus reading on the most relevant sources and surface connections across your own corpus.
- Improve reproducibility — Document which tools you used and how you verified results; many institutions and journals now expect this.
- Assist writing and clarity — Use AI to draft or polish, then revise and verify; always cite and evaluate.
Top 4 AI tools for research
- Semantic Scholar — Free semantic search, TL;DRs, and citation analysis.
- Google NotebookLM — Upload papers and get Q&A and audio overviews over your corpus.
- Scite — Citation context: see whether later work supports or contrasts each claim.
- Undermind — High-quality semantic search and ranking with minimal AI summaries to encourage reading primary sources.
1. Semantic Scholar — free semantic search and citation analysis
Semantic Scholar uses AI to understand the meaning of research, not just keywords. You get short TL;DR summaries, citation graphs, and filters by methodology and impact. It’s free and particularly strong in computer science, biomedicine, and neuroscience.
Why scientists use Semantic Scholar
- Free access to a very large paper index with semantic search.
- TL;DR summaries and research feeds that surface relevant work across terminology.
- Citation analysis to find seminal papers and track how ideas evolve.
- Filtering by methodology, recency, and impact for systematic reviews.
How to get started
Try Semantic Scholar — search by topic or paste a paper title/abstract to find related work. Use the filters and citation views to narrow and explore.
Best for: Literature discovery, staying current in your field, and finding key papers for grants and reviews.
2. Google NotebookLM — Q&A over your own papers
NotebookLM lets you upload your own set of papers and build a personalized AI that answers questions from that corpus. Responses are grounded in your sources, which reduces hallucination. Audio overviews let you review material on the go.
Why scientists use NotebookLM
- Questions that span multiple papers in your library.
- Connections and comparisons across your uploaded sources.
- Summaries and answers tied to specific passages in your documents.
- Audio overviews for reviewing complex material.
How to get started
Open Google NotebookLM — upload PDFs or paste links, then ask questions. Use it to compare methodologies, limitations, or findings across your reading list.
Best for: Dissertation and grant literature reviews, keeping a "second brain" over your reading, and cross-paper synthesis.
3. Scite — citation context and reliability of claims
Scite shows not just who cited a paper but how: supporting, contrasting, or merely mentioning. That helps you assess whether a finding has been replicated or contested and avoid building arguments on shaky or overturned results.
Why scientists use Scite
- Smart Citations with context: supporting vs contrasting citations.
- Assessment of how claims have been received in subsequent literature.
- Large citation index to trace influence and controversy.
- Better quality control before citing a paper in a review or grant.
How to get started
Visit Scite — search for a paper or claim and review the citation context. Use it when you need to verify that key evidence is still supported by later work.
Best for: Verifying citation context, assessing reliability of key papers, and avoiding reliance on contradicted or weak evidence.
4. Undermind — semantic search that encourages reading primary sources
Undermind emphasizes high-quality search and ranking with minimal AI summarization. The goal is to surface the right papers and encourage you to read the originals rather than rely on short AI digests—a good fit for rigorous, citation-heavy work.
Why scientists use Undermind
- Strong semantic search and ranking methodology.
- Minimal AI summaries so you engage with full papers.
- Useful when you need to cite and interpret primary sources carefully.
- Reduces over-reliance on secondhand AI summaries.
How to get started
Explore Undermind — use it for discovery and ranking, then read and cite the papers yourself. Ideal for grant writing and methods-heavy literature where nuance matters.
Best for: Researchers who want powerful discovery without substituting AI summaries for reading primary literature.
Best practices: use AI responsibly
Libraries and institutions recommend a simple framework for ethical AI use. Combine it with critical warnings and clear citation practices.
Responsible use — the A.C.E. framework
- Acknowledge your use of AI tools in your work (in methods, footnotes, or disclosure statements as required).
- Cite the tool in your bibliography or references when you rely on its output.
- Evaluate the information for errors and verify every reference against legitimate academic databases or the original source.
Critical warnings
- AI tools can "hallucinate" — generating false or non-existent references. Research in Nature shows that general-purpose LLMs can invent a large share of cited papers; tools that ground answers in real literature (retrieval-augmented systems) are far safer.
- Never upload sensitive, confidential, or proprietary information (e.g. unpublished data, patient information, grant drafts) into third-party AI tools.
- Citations from AI tools must be verified against legitimate academic databases or the publisher's site before you cite them.
- Copyright restrictions may apply to uploading full articles to some tools; check terms of use and institutional guidance.
Citing AI
In many style guides, AI-generated content is treated similarly to personal communication: acknowledge and cite the tool. For discipline-specific guidance (APA, MLA, Chicago, etc.), see UConn Library's Citation Styles and AI.
References and further reading
- AI@UCSF (institution): AI@UCSF Home and Platforms, Tools and Resources — institutional AI guidance, supported chatbots (e.g. UCSF ChatGPT Enterprise, Versa Chat), meeting support, productivity tools, research/HPC tools, and the GenAI community.
- UConn Library: Artificial Intelligence: A Guide for Students & Faculty and AI Tools for Research — A.C.E. framework, warnings, and tool overview.
- Nature: Synthesizing scientific literature with retrieval-augmented language models (and Table 2: statistics on hallucinated papers) — evidence on why retrieval-grounded tools reduce citation hallucination.
- apxml.com: Best Local LLMs to Run On Every Apple Silicon Mac in 2026 — for scientists on Mac who want local/private inference (e.g. Ollama).
