AIScholar for social science researchers
Language models are becoming objects of social-scientific study — they negotiate, advise, judge, and display the biases of the text they learned from. AIScholar gives you the full experimental apparatus you already know — factorial designs, manipulation checks, mixed models — pointed at a new kind of subject.
Your methods transfer almost directly
If you have run a vignette experiment, you can run an LLM experiment. The mapping is nearly one-to-one — and where it is not, the differences work in your favor:
| In a human study | In AIScholar |
|---|---|
| Vignette conditions | Factor levels realized as prompt snippets — the manipulation is versioned text, byte-for-byte reproducible |
| Participant sample | Replications: the same condition sampled dozens of times per model at a chosen temperature |
| Between- vs. within-subjects | Crossing strategies: full factorial, fractional, matched-pair, or custom condition subsets |
| Coding open-ended answers | LLM-as-a-judge with human calibration, agreement statistics, and versioned rubrics |
| IRB, consent, recruitment | Not applicable — a study that takes a semester with participants takes an afternoon |
The important disanalogy: replications are not independent participants but draws from one model's response distribution. AIScholar's analysis stage treats them accordingly — mixed models with condition and model structure are first-class, not an afterthought.
Questions social scientists are asking with it
The example walkthroughs closest to social-science practice, each a complete design-to-write-up path:
- Framing effects across modelsThe Asian-disease paradigm, rebuilt as a matched-pair LLM study with a decoupling-gap analysis.
- Persona × task factorialA 3×3 interaction study: does an assigned persona change advice, and does it depend on the task?
- Prompt sensitivity as a robustness checkTreat wording as a random effect and report how much of your effect is phrasing.
- Analyzing found dataAlready have model outputs from another project? Import, code, and analyze them honestly.
Rigor a reviewer will recognize
The platform is built around the methodological standards your field already enforces. Twelve rule-based warnings flag issues like underpowered cells and missing manipulation checks. Every prompt, parameter, and model version is captured for the reproducibility package, and the write-up assistant drafts Method sections in the register of your journals — with a citation library grounded in the machine-behavior literature.
- Experimental designFactors, levels, crossing strategies, and power-aware replication planning.
- Prompt constructionSnippet library and variation grid: your manipulations as versioned, reviewable text.
- Analysis & visualizationANOVA, chi-square, mixed models, effect sizes — plus an AI analyst that shows its work.
- Write-up assistanceAI-drafted Method and Results grounded in your actual design and outputs.
What to be honest about
LLM findings are claims about models, not people: response distributions under a sampling procedure, sensitive to prompt wording and model version. AIScholar keeps those caveats in the workflow — wording-robustness checks, model-version capture, and write-up drafts that scope claims correctly — so the honesty is built in rather than bolted on. Start with the example studies or the full feature tour.
AIScholar for other researchers
- Psychology researchersAdapt validated instruments, run psychometric checks, and study machine behavior with familiar methods.
- AI-safety & behavioral researchersMeasure refusals, sycophancy, and robustness with reproducible, statistically defensible methods.
- Educators & methods instructorsTeach the full arc of experimental research — design, execution, analysis, write-up — without an IRB or participant pool.
Turn a question about LLMs into published research.
Start your first study today. No human participants required.