AI·기술

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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

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.

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 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.

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.

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.

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

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.