Start from a proven structure, then edit as needed.
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Assesses how users find, customize, and derive value from a shared AI prompt library. Use this to identify discovery friction, reuse patterns, and outcome perceptions to prioritize product improvements.
View templateA controlled comparison instrument for evaluating interview experiences across different moderator formats. This survey measures pre-interview expectations, embeds an interview session, and captures post-interview evaluations of comfort, quality, depth, trust, and willingness to participate again.
View templateA repeated-measures survey template designed to track how participant comfort, trust, and naturalness perceptions of AI interviewers evolve across multiple sessions. Administer at each study wave with consistent scaling to enable within-subjects change analysis.
View templateMeasures perceived realism, fit accuracy, and purchase confidence for augmented reality try-on features. Designed for e-commerce UX researchers seeking to identify AR experience gaps that drive returns and reduce conversion.
View templateCollects structured stakeholder feedback on red-team risk coverage, report quality, and remediation follow-through to identify actionable program improvements across security, engineering, and leadership functions.
View templateAssess how content filter false positives affect developer productivity, workflow disruption, and tool adoption decisions. Designed for developer experience researchers and tooling teams seeking actionable improvement priorities from software practitioners.
View templateA stimulus-comparison survey for UX researchers and AI product teams to evaluate the clarity, tone, and helpfulness of AI safety and refusal messages. Produces actionable data on user preferences and improvement priorities.
View templateMeasures user expectations for AI agent autonomy, preferred escalation and handoff mechanisms, permissible actions, spending thresholds, and risk concerns. Designed for UX researchers and product teams building agentic AI workflows.
View templateMeasures consumer expectations for AI transparency across products and services, capturing preferred disclosure methods, acceptability thresholds, and trust drivers to inform product labeling and policy decisions.
View templateMeasures user experiences with AI errors, recovery preferences, and resulting trust impact. Designed for AI product teams seeking to prioritize reliability improvements and reduce error-driven churn.
View templateMeasures consumer trust in generative AI tools, perceived safety risks, transparency expectations, and guardrail preferences to inform responsible AI product design and policy.
View templateMeasures consumer awareness, trust, acceptability, and behavioral intentions regarding AI content provenance watermarks, designed for technology policy researchers and platform designers evaluating labeling strategies.
View templateAn internal stakeholder survey evaluating scope clarity, decision fairness, and incentive effectiveness in your AI bug bounty program over the past 6 months to guide program improvements.
View templateMeasures software developers' perceptions of fairness, bias, and representativeness in their evaluation practices. Ideal for engineering leadership and DEI teams seeking to identify gaps in evaluation methodology and build more inclusive processes.
View templateMeasures how users perceive the clarity, usefulness, and behavioral impact of AI product changelogs. Designed for product and developer experience teams seeking to optimize release communication and drive feature adoption.
View templateA research survey exploring how faculty and students adopt, perceive, and experience AI tools in higher education settings. Covers current usage patterns, perceived benefits and barriers, institutional policy awareness, training needs, and impact on teaching and learning outcomes.
View templateMeasures user adoption, satisfaction, trust, and pain points with AI-assisted product features. Use it to capture actionable feedback that informs product roadmap and feature prioritization decisions.
View templateMeasures user attitudes toward AI transparency, desired controls, and recourse expectations. Designed for product teams assessing trust gaps and prioritizing AI governance improvements.
View templateMeasures VR players' motion sickness susceptibility, symptom frequency, discomfort triggers, and comfort technique preferences to guide UX design decisions for virtual reality games and experiences.
View templateMeasures user interest, perceived value, adoption barriers, and willingness to pay for AI-powered product features. Designed for SaaS product teams prioritizing their AI roadmap based on user feedback.
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