Expected Parrot

Researchers at academic institutions and other organizations need to design, pilot, and run studies such as surveys, interviews, and experiments, but doing this well requires scoping questions, drafting instruments, recruiting respondents, and rebuilding the same study across different tools for AI and human testing. Without a shared workflow, teams lose time rebuilding instruments when moving between AI piloting and human fielding, and it is hard to verify, reproduce, or trace conclusions back to underlying data. Testimonials describe researchers using separate scripts or ad hoc Python work to interact with AI models for tasks like data labeling and transcript analysis, suggesting this was previously a manual, technical lift.

Expected Parrot provides one workflow for scoping a research question, designing a study (questions, interview guides, respondent groups, scenarios), testing it with AI respondents for fast iteration, then fielding the same instrument to real people, and comparing results across humans, models, and methods. It supports AI-led interviews, structured surveys, screeners, scales, and open-ended studies within a single environment, so instruments and respondent groups don't need to be rebuilt for each method. The platform emphasizes auditability and control: every question, response, model choice, and result is visible, results can be exported, and a caching feature enables reproducibility and reduces computing costs across experimental iterations. It is also built around an open-source Python package (edsl) that researchers can use directly, alongside hosted features like a Research Agent, a study builder, and an AI interviewer.