Features
One platform for the whole study.
AIScholar takes an LLM experiment from hypothesis to publishable write-up in a single, disciplined pipeline. Each capability area below covers one part of that journey in depth — what it does, how it works, and where it fits.
Capability areas
- Hypotheses & experimental designFrame testable predictions, then build factorial, fractional, matched-pair, or custom designs with an AI design wizard.Learn more
- Prompt constructionTurn factor levels into concrete prompts with a versioned snippet library, a variation grid, and an AI prompt wizard.Learn more
- Experiment executionDispatch trials to any OpenRouter model with concurrency, retries, pilot runs, prompt variants, and cost estimates.Learn more
- Response codingScore responses with LLM-as-a-judge, check agreement across judges, and keep humans in the loop with gold-standard review.Learn more
- Analysis & visualizationRun mixed models, ANOVA, Bayesian and non-parametric tests, LLM-specific diagnostics, and talk through results with an AI Analyst.Learn more
- Write-up assistanceDraft manuscript sections with AI, manage citations in five styles, verify references, and export to Word, Markdown, or LaTeX.Learn more
- ReproducibilityVersioned prompts and coding schemes, full trial metadata, a Method Specification Prompt, and one-click reproducibility packages.Learn more
- R&D documentationCapture uncertainty statements, a contemporaneous activity log, and effort timesheets as you work — then export the evidence package.Learn more
New to the platform? The overview on the home page walks the nine-stage pipeline end to end, and every feature page cross-links to the stages before and after it, so you can follow a study through the whole workflow.
Turn a question about LLMs into published research.
Start your first study today. No human participants required.