6 AI Tools You'll ACTUALLY Use For Research

For researchers drowning in PDFs, spreadsheets, and literature searches, the most practical AI advantage is speed: turning messy inputs into usable summaries, tables, and citation trails. The core daily workflow centers on ChatGPT Plus, largely because it provides access to the GPT-4 model and can work without automatically browsing the internet—useful when a researcher feeds it specific text, then asks for summaries, structured outputs, or formatted tables that would otherwise take hours.

ChatGPT Plus becomes even more effective when paired with Text Blaze, a tool for reusable writing and editing templates. The transcript describes using Text Blaze to route different tasks—such as drafting introductions, rewriting sections, or handling YouTube- and language-learning-related prompts—by feeding the relevant text into ChatGPT and then requesting targeted transformations. A key upgrade mentioned is GPT-4’s Code Interpreter, which is used for data-heavy work like extracting information from Excel documents and converting raw information into Excel-ready tables. For anyone pulling data from multiple sources, the appeal is straightforward: AI handles the formatting and tabulation pain that typically slows researchers down.

For the “science-first” literature layer, the transcript recommends SciSpace’s Site Assist (referred to as “site assist assistant”). The emphasis is on asking simple questions and receiving full-text support drawn from millions of research articles, plus help with writing and using evidence to strengthen research. When input limits hit—ChatGPT’s character limits—the workflow shifts to a more flexible approach: using ChatGPT in ways that support audio, YouTube, websites, and file uploads, including local, temporary storage of files. The goal is to keep research inputs close at hand without building a complex vector database (the transcript specifically notes rarely needing tools like Pinecone).

Another near-daily tool is Elicit, described as free and frequently used for quick “what does the evidence say?” checks. Elicit can upload PDFs and, in a newer version being trialed, adds the ability to build a personal library of papers and then run workflows such as extracting information from PDFs, generating tables of synthesized concepts, and adding custom columns like summary fields. The tradeoff is cost: extracting and processing papers consumes credits, though the free tier is portrayed as sufficient for many researchers.

Once a seed paper is selected, the transcript shifts from extraction to navigation. Litmaps and Connected Papers are used to map derivative and prior work—especially to see what has cited a paper and to identify what’s changed since the original publication. The practical pattern is: use Elicit or Site Assist to find a relevant starting point, then use citation and network tools to locate the leading edge of the field.

Overall, the message is less about chasing the newest AI and more about building a personal toolkit: try tools, keep what fits the workflow, and drop what doesn’t. The productivity gains only matter if they translate into faster evidence gathering, clearer synthesis, and better direction in the literature.