Large Language Models are technology that may help automate several tasks associated with research (in addition to many other applications). They are trained on text and can help with reasoning tasks, answering questions, and searching along the citaiton chain for scientific literature. They are changing rapidly, but currently they show promise for research but certainly don't replace the need for critical thinking and subject expertise.
| AI Tool | Best Research Use | Cost | Features at a Glance |
|---|---|---|---|
|
|
Developing Research Topics; Background Research |
Free |
Pros: Have students ask the chat to ask them questions about their topics or give an overview. Use a specific prompt and ask Chat GPT to provide citations. Cons: Students must check these citations to make sure they are real. Tools like Chat GPT may "hallucinate" and provide citations that are not actual sources. |
| Perplexity |
Developing Research Topics; Background Research |
Free Pro-version: $20/month |
Pros: Ask Perplexity a research question and it will answer your question with web-based sources. Cons: Students should evaluate how accurate these responses are, and look closely at the sources returned to make sure these are the best version to answer. Ethical concerns with copyright and how Perplexity is gaining access to sources. |
| Research Rabbit |
Visualizing scholarly conversation; Finding scholarly articles |
Free |
Start with a seed article and then visualize linkages between scholarly citations and authors. You can look at a timeline of articles published about a topic, or find related authors or citations. Cons: Are these citation mappings comprehensive? It's unclear. Research Rabbit hasn't published much about their methodology. |
| Connected Papers |
Visualizing scholarly conversation; Finding scholarly articles |
Free: 5 graphs/month Pro: $6.00/month |
Pros: Visualization tool for researchers in "applied sciences." Similar to Research Rabbit. Cons: Not comprehensive, some learning curve. |
| Elicit |
Developing a Literature Review; Extracting data for Systematic Reviews |
Start with 5,000 search credits and after that $12/month |
Pros: Ask a question and get a research-backed answer linking to top papers. There will also be a grid pulling information from papers such as brief summary, sample size and location, study type, and much more. It is responsive and customizable. Cons: As always, students and faculty must evaluate the accuracy of information pulled from sources. Elicit is a powerful tool built to look at scholarly literature, but can "hallucinate" data or other information pulled from sources. |
| Scite.ai Assistant |
Developing a Literature Review; Extracting data for Systematic Reviews |
3 Free Searches, and after that $20/month |
Pros: Ask a question and get a research-backed answer with in-text citations. Produces literature review matrix. Can control types of sources cited. Cons: Pricing doesn't allow for much free use. Must be skeptical of ai-generated summaries and top papers. |
| Scholarcy | Read and Understand Scholarly Articles |
Free: 3 summaries/day Pro version: $10/month |
Pros: Load a paper into Scholarcy to extract a summary or highlight important parts of the paper to assist with interpreting scientific literature. Create flash cards that outline how the paper relates to other papers in the field. Cons: As always must evaluate the accuracy of summaries and analysis. |
For more comprehensive listing of AI Research Tools, see Aaron Tay's Musings about Librarianship.
