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 |
| Model-only trials and review | Model trials do not recruit people; human data, raters, or institutional policy may still require review |
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
Example walkthroughs close to social-science practice:
Framing effects across models
The Asian-disease paradigm, rebuilt as a matched-pair LLM study with a decoupling-gap analysis.Persona × task factorial
A 3×3 interaction study: does an assigned persona change advice, and does it depend on the task?Prompt sensitivity as a robustness check
Treat wording as a random effect and report how much of your effect is phrasing.Analyzing found data
Already have model outputs from another project? Import, code, and analyze them honestly.
Methods support and researcher review
Workflow checks can flag design concerns, but do not replace methodological review. Inspect the prompts, parameters, and model information available in the reproducibility exports. The write-up assistant can draft methods text; verify statements and citations against the actual study.
Experimental design
Factors, levels, crossing strategies, and power-aware replication planning.Prompt construction
Snippet library and variation grid: your manipulations as versioned, reviewable text.Analysis & visualization
ANOVA, chi-square, mixed models, effect sizes — plus an AI analyst that shows its work.Write-up assistance
AI-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. Researchers should account for these limits when interpreting results. Start with the example studies or the full feature tour.
AIScholar for other researchers
Psychology researchers
Adapt validated instruments, run psychometric checks, and study machine behavior with familiar methods.AI-safety & behavioral researchers
Explore bounded questions about sycophancy, honesty, uncertainty and refusal behavior without treating text outputs as proof of safety.Educators & methods instructors
Teach experimental methods with model-response studies while checking institutional requirements for human data and review activities.
Study LLM behavior with a guided research workflow.
Design controlled text studies, collect model responses, and explore what changes across conditions. You decide what the evidence means.