교육·학술

For K-12 schools that want to understand why families disenroll, this survey captures the stated reason, satisfaction across key factors, and where the child is heading next. An AI follow-up interview digs past the checkbox answer to the real timeline of dissatisfaction and what might have changed the outcome — insight schools can act on to reduce future attrition.

A validated self-assessment instrument measuring undergraduate AI literacy across five dimensions: conceptual understanding, practical skills, critical evaluation, ethical reasoning, and awareness of limitations. Estimated completion time: 10-12 minutes.

최근 학부모-교사 상담에서 학부모가 얼마나 준비되어 있었고, 자신의 의견이 경청되었으며, 충분한 정보를 얻었다고 느꼈는지, 실제로 어떤 주제가 다루어졌는지, 그리고 명확한 다음 단계 계획이 마련되었는지를 파악합니다 — AI 후속 질문을 통해 제기된 구체적인 우려 사항과 그것이 해결되었는지 여부를 재구성합니다. 상담 품질을 점검하는 학교 및 학부모회를 위해 제작되었습니다.

Measures awareness decay of recently launched features across user segments, identifying which communication channels sustain visibility and where messaging drop-offs occur post-launch.

A team-level diagnostic for mixed-methods research projects, evaluating convergence, divergence, and integration rigor across qualitative and quantitative strands to guide analytic next steps.

Measures how clearly employees understand what it takes to move up, how well current training and development options meet their needs, and what's actually slowing their progress — with an AI follow-up that reconstructs a real advancement attempt instead of collecting generic complaints about 'lack of opportunity.'

Measures how confident parents and caregivers feel across the specific tasks of supporting a child's education — homework help, motivation, school communication, and behavior management — plus an AI follow-up that digs into the real story behind their toughest challenge. Built for schools, districts, and parenting programs assessing where families need more support.

연구 참여자의 스터디 세션 길이, 휴식 일정, 보상 구조에 대한 선호도를 측정합니다. 참여자의 편안함과 데이터 품질을 위해 스터디 설계를 최적화하려는 학계 및 UX 연구자를 위해 고안되었습니다.

Measures what drives students to show up, engage, and keep working across a course or term — effort, interest, and barriers — with an AI follow-up that digs into the real reason behind their biggest motivation dip.

생성형 AI 도구가 학습 심화, 비판적 사고, 지식 유지, 메타인지 인식 전반에 걸쳐 학생의 학습 성과에 어떻게 영향을 미치는지 측정하는 연구 도구입니다. 사전/사후 및 실험 연구 설계를 위해 고안되었습니다. 예상 소요 시간: 10~14분.

A structured self-reflection for teachers covering instructional practices, classroom management, student engagement, and professional growth priorities, plus an AI follow-up interview that digs into a specific teaching moment behind the numbers instead of stopping at a rating.

Tracks how satisfied teachers are with workload, administrative support, pay, resources, and school culture, and flags retention risk before it shows up in resignation letters. An AI follow-up interview digs into the specific incident behind each teacher's rating instead of settling for a number. Built for school leaders and district HR running an annual or mid-year staff pulse.

Captures what learners already know, what they expect to get out of an upcoming training program, and how confident they feel going in — so facilitators can tailor content instead of guessing. An AI follow-up interview digs into the specific skill gap or obstacle behind each learner's biggest concern. Built for L&D teams and instructors running any workshop, course, or certification.

Understands why students leave high school before earning a diploma and what conditions might bring them back, for school districts, counselors, and youth-reengagement programs. An AI follow-up interview reconstructs the specific turning point behind the decision to leave, going deeper than a single checkbox reason.

A structured instrument for school and district leaders to quantify educator administrative burden, identify top time sinks, and assess readiness for workflow automation. Designed for K–12 teaching staff and instructional support roles.

Collects structured employer feedback on intern performance, program structure, supervision investment, and organizational outcomes to guide internship program improvements.

Measures student satisfaction, instructional quality, curriculum relevance, and near-term learning transfer for fintech courses. Designed for course administrators and instructional designers seeking actionable improvement data.

Measures where employees feel skill gaps, which learning formats and time investments actually work for them, and how their career goals should shape the L&D roadmap. Built for HR and L&D teams planning training budgets and programs. An AI follow-up digs into the specific skill gap and career goal each person names to surface concrete blockers, not just checkbox preferences.

교육 세션이 어떻게 전달되었는지 — 콘텐츠 품질, 강사의 효과성, 새로운 기술을 적용하는 자신감 — 를 파악하여 L&D 팀과 강사에게 제공합니다. AI 후속 인터뷰는 추천 점수 이면의 실제 이야기를 파고듭니다: 배운 내용을 활용하는 데 구체적으로 무엇이 도움이 되었거나 방해가 되었는지 알아봅니다.

An academic research survey exploring faculty attitudes, concerns, and readiness regarding AI-assisted grading and assessment tools. Covers current practices, openness to adoption, concerns about bias/accuracy/privacy, training needs, and willingness to participate in controlled experiments.