Start from a proven structure, then edit as needed.
Filter: AI & Technology
Measures developer experience, tooling preferences, risk perceptions, and adoption intent for synthetic data. Designed for engineering and data science teams evaluating synthetic data readiness and ethical boundaries.
View templateMeasures how AI users experience error reporting workflows and how unresolved issues affect trust and future reporting intent. Designed for product and UX teams seeking to reduce reporting friction and improve AI reliability perceptions.
View templateAn operational audit survey for data labeling teams, measuring instruction clarity, bias mitigation practices, QA rigor, and workflow bottlenecks over the last 30 days. Designed for labelers, reviewers, and QA leads.
View templateCaptures frontline warehouse worker feedback on safety conditions, throughput changes, role clarity, and operational tools to identify improvement priorities and support OSHA compliance.
View templateMeasures AI tool adoption rates, usage barriers, quality-speed tradeoffs, and credit/disclosure norms among media creators across disciplines. Suitable for creative industry researchers and platform teams studying the creator-AI relationship.
View templateA survey studying how research teams evaluate, adopt, and integrate AI tools for data collection, analysis, and reporting. This instrument measures current tool usage, evaluation criteria, adoption barriers, training experiences, data quality perceptions, and team collaboration patterns.
View templateMeasures how ML/AI practitioners engage with model cards, evaluate documented limitations, and how documentation quality shapes trust and adoption decisions across deployment contexts.
View templateMeasures consumer awareness, trust, privacy concerns, and adoption intentions regarding on-device AI training. Designed for product, UX, and privacy teams seeking to understand how users perceive and evaluate local AI learning features.
View templateMeasures organizational readiness across AI policy clarity, approval workflows, risk tiering, and control maturity. Designed for cross-functional teams involved in AI development, deployment, or oversight.
View templateAssesses organizational readiness across edge AI governance, monitoring, risk, and MLOps practices. Designed for AI/ML leaders, DevOps, and compliance stakeholders to benchmark maturity and prioritize investment.
View templateMeasures employee AI tool usage patterns, shadow AI risks, policy awareness, and training needs to inform governance and safe-adoption strategies across the organization.
View templateTell us your research goal and we can help design a template.