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Honest comparison

AIScholar vs. survey platforms & DOE software

Survey platforms are superb at studying humans; design-of-experiments software is superb at optimizing physical processes. When the research subject is a language model, both leave most of the pipeline to you.

What these tools do well

Survey platforms give social scientists mature instruments for human participants: questionnaire logic, randomization, panel recruitment, consent flows, and response quality checks. Statistical design-of-experiments software gives engineers optimal designs for physical processes: response-surface methods, blocking, screening designs, and process-optimization analytics.

If you are studying people, use a survey platform (and an IRB). If you are optimizing a chemical yield, use DOE software. AIScholar replaces neither.

What changes when the subject is a model

  • The participant is an API

    There is no panel to recruit — trials dispatch to any OpenRouter model with orchestrated concurrency, retries, and per-run cost estimates on your own key. Survey tools have no concept of this.

  • The questionnaire is a prompt

    Conditions are realized as text: a snippet library binds wording variants to factor levels, templates are versioned, and every prompt instantiation is previewable before a single trial runs.

  • Replication replaces sampling

    With humans you sample participants; with models you sample responses. Power comes from replications per condition, and consistency itself (semantic entropy) becomes an outcome variable.

  • Responses need machine coding

    Free-text answers at LLM scale need LLM-as-a-Judge coding with human calibration and agreement statistics — a stage neither survey platforms nor DOE tools provide.

  • Validity threats are different

    Instead of demand effects and attrition, you face training-data contamination, in-context learning, and prompt-wording sensitivity — with dedicated detection tools for each.

  • Statistics stay familiar

    Mixed models, factorial ANOVA, GLMMs, Bayesian tests, TOST — the analysis toolkit social scientists already know, applied to model-generated data without exporting to R midway.

When to use which

Use a survey platform for human studies, and DOE software for industrial process optimization. Use AIScholar when the research subject is a language model and you want the whole arc — design, prompt construction, execution, coding, analysis, write-up — in one place, with the LLM-specific methodology built in rather than bolted on.

Familiar by design

AIScholar deliberately borrows the survey researcher's mental model: factors and levels, instruments and items, replications and reliability. If you have run factorial vignette studies on humans, the workflow will feel recognizable — the platform's job is translating that discipline to a machine population.

See how the translation works in practice in AIScholar for social scientists or the 3×3 factorial walkthrough.

All comparisons

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