AI·기술

Measures 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.

Measures how well an AI scheduling chatbot helps patients book, reschedule, or get answers about medical appointments, covering task completion, trust, and friction points — with an AI follow-up interview that digs into the specific moment the chatbot helped or failed.

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.

Measures 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.

데이터 라벨링 팀을 위한 운영 감사 설문으로, 지난 30일간의 지침 명확성, 편향 완화 관행, QA 엄격성, 워크플로 병목 현상을 측정합니다. 라벨러, 리뷰어, QA 리드를 대상으로 설계되었습니다.

현장 창고 근로자로부터 안전 조건, 처리량 변화, 역할 명확성, 운영 도구에 대한 피드백을 수집하여 개선 우선순위를 파악하고 OSHA 준수를 지원합니다.

Measures 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.

A 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.

Measures how ML/AI practitioners engage with model cards, evaluate documented limitations, and how documentation quality shapes trust and adoption decisions across deployment contexts.

Evaluates how well an airline or travel site's AI chatbot handles real booking, change, and support tasks — covering task completion, trust, and where users bail out to a human. An AI follow-up interview reconstructs exactly what happened in the respondent's most recent chatbot session, not just how they'd rate it in hindsight.

온디바이스 AI 학습에 대한 소비자 인지도, 신뢰, 프라이버시 우려, 채택 의향을 측정합니다. 사용자가 로컬 AI 학습 기능을 어떻게 인식하고 평가하는지 이해하고자 하는 제품, UX, 프라이버시 팀을 위해 설계되었습니다.

Measures 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.

Measures how much time and cognitive effort employees spend checking AI agent outputs, where trust is over- or under-calibrated, and what triggers a full manual re-check. An AI follow-up probes the last time output was wrong or nearly acted on unchecked.

Assesses 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.

Measures employee AI tool usage patterns, shadow AI risks, policy awareness, and training needs to inform governance and safe-adoption strategies across the organization.