AIScholar for educators and methods instructors
The hardest part of teaching research methods is letting students actually run studies: recruitment, IRB timelines, and one shot at data collection. With language models as subjects, every student can design, run, analyze, and write up a real experiment — several times in one term.
The whole research arc, inside one course
A student project in AIScholar exercises every competency a methods sequence tries to teach — on a timeline that fits a syllabus:
| Concept you teach | Where students practice it |
|---|---|
| Falsifiable hypotheses | Stage 1: registered predictions with rationales, checked for feasibility |
| Operationalization | Factors and levels realized as prompt snippets — the manipulation is visible, editable text |
| Factorial design & interactions | Crossing strategies and the variation grid showing every condition before it runs |
| Sampling & replication | Replications per cell with cost estimates — a concrete power-vs-budget tradeoff |
| Measurement & reliability | Coding schemes, judge calibration, inter-rater agreement statistics |
| Statistical inference | ANOVA, chi-square, mixed models, effect sizes on data students collected themselves |
| Scientific writing | AI-drafted Method/Results the student must verify against their own outputs — a built-in lesson in checking sources |
Because a full cycle costs hours instead of a semester, students can fail, redesign, and re-run — the iteration loop real research has and coursework almost never allows.
Ready-made assignments
The example walkthroughs double as assignment templates. Each one is a complete, runnable design with the methodological reasoning spelled out:
- Framing effectsA classic paradigm students know from lectures, rebuilt as a matched-pair LLM study.
- Persona × task factorialThe cleanest teaching example of a 3×3 interaction — and why main effects aren't the whole story.
- Coding open-ended responsesA measurement-focused assignment: rubric writing, calibration, and agreement statistics.
- Psychometric assessmentFor advanced seminars: reliability, alternate forms, and contamination — measurement theory in action.
Guardrails that teach
The platform's methodological warnings — twelve rule-based checks for underpowered cells, missing manipulation checks, confounded designs, and more — act as a patient TA: students see why a design is weak at the moment they can still fix it. Versioning keeps an audit trail of every design decision, which makes grading the process (not just the final PDF) practical.
- Experimental designFactors, levels, and crossing strategies with feasibility checks built in.
- Prompt constructionThe variation grid makes 'operationalization' something students can see.
- Analysis & visualizationReal statistics on real data — with an AI analyst that explains, not just computes.
- Write-up assistanceDrafts students must verify and revise — AI-assisted writing done honestly.
What it teaches about AI, too
A side effect worth having: students who run controlled experiments on language models come away with calibrated intuitions about what these systems are — distributions of text behavior, sensitive to wording, varying across versions — instead of folk theories. For many programs that is a learning objective in itself. See the example studies for assignment-ready designs, or the feature tour for the full platform.
AIScholar for other researchers
- Social & management scholarsBring experimental rigor to questions about framing, decision-making, and judgment in language models.
- 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.
Turn a question about LLMs into published research.
Start your first study today. No human participants required.