AI & technology survey templates

사람들이 엔터테인먼트 챗봇(친구 역할, 롤플레이, 유머, 스토리텔링)을 얼마나 자주 사용하는지, 무엇이 재방문을 유도하는지, 그리고 경험이 어디에서 기대에 못 미치는지를 측정합니다. 또한 AI 후속 질문을 통해 기억에 남는 특정 대화를 재구성하여 실제로 무엇이 재미있고, 그럴듯하며, 혹은 실망스러웠는지를 드러냅니다. 캐릭터 또는 엔터테인먼트 중심의 AI 경험을 출시하는 제품 및 콘텐츠 팀을 위해 제작되었습니다.

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.

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 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.

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.

A 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.

A 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.

A 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.

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

Measures VR players' motion sickness susceptibility, symptom frequency, discomfort triggers, and comfort technique preferences to guide UX design decisions for virtual reality games and experiences.

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

A 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.

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.

Measures 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.

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.

Measures 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.

생성형 AI 도구에 대한 소비자 신뢰, 인지된 안전성 위험, 투명성 기대치, 가드레일 선호도를 측정하여 책임 있는 AI 제품 설계 및 정책 수립에 활용합니다.

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 user attitudes toward AI transparency, desired controls, and recourse expectations. Designed for product teams assessing trust gaps and prioritizing AI governance improvements.

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.

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.

Measures consumer awareness, trust, acceptability, and behavioral intentions regarding AI content provenance watermarks, designed for technology policy researchers and platform designers evaluating labeling strategies.

Assess 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.

Measures 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.

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.

사용자가 AI 제품 변경 로그의 명확성, 유용성, 행동적 영향을 어떻게 인식하는지 측정합니다. 릴리스 커뮤니케이션을 최적화하고 기능 도입을 촉진하려는 제품 및 개발자 경험 팀을 위해 설계되었습니다.

Collects 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.

An 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.

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

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 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 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.

Measures 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.

Measures 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.