LearnFork for Researchers

The problem with linear chat

Research is non-linear. You start with a thesis, the AI cites a study, you want to explore that study's methodology, which raises questions about sample size validity, which connects to a different statistical approach. In a linear chat, this becomes an unnavigable wall of text.

The LearnFork way

Your conversation tree mirrors your research structure:

• Vertical (keep chatting) — develop your main argument or literature review in a straight line
• Horizontal (branch) — explore each citation, claim, or methodology in its own branch. Sub-branch for deeper dives. Your canvas becomes a visual research map.

Step by step: literature review

• Start with your research question: "What does current research say about [topic]?"
• The AI outlines key findings and studies. Branch from each one to explore it individually.
• In each branch, ask about methodology, sample size, limitations, and counter-arguments
• If a branch raises a new question, sub-branch from it. Go as deep as the research takes you.
• Back on the canvas, you can see your entire research landscape — main argument in the center, supporting evidence branching out

Building an argument map

Start with your thesis. Branch for each supporting argument. From each supporting argument, branch to explore counter-arguments. From counter-arguments, branch to explore rebuttals. The tree structure naturally maps the logical structure of your argument.

Tips

• Pin key findings so you can review them all in one place on the Pins page
• Use "Go deeper" to push the AI on weak claims
• Select specific claims and use "Quote" to branch with that claim as context for targeted follow-up
• Your canvas layout mirrors your research structure — use it as a visual outline
• Use "Google" on cited study names to verify them against real sources