교육·학술

A pre-study instrument that verifies participants' understanding of consent information, risks, and withdrawal rights. Designed for IRB compliance and suitable for any human subjects research protocol.

A structured end-of-term evaluation measuring student perceptions of instruction quality, course materials, workload, and pace. Designed for higher-education institutions seeking actionable feedback to improve course design and teaching effectiveness.

Captures how borrowers experience student loan repayment — balance size, plan type, servicer satisfaction, and financial stress — plus a best-worst ranking of support options and an AI follow-up interview that digs into the specific moment repayment felt confusing, unaffordable, or manageable. Built for financial wellness teams, lenders, and student support offices.

A true factorial vignette experiment: respondents judge realistic scenarios whose key details (price framing, messenger, wording) rotate systematically, so you can measure how each factor shifts judgment. The built-in vignette question type generates the scenario combinations for you.

Builds a demographic and educational-background profile of students or learners — current education level, enrollment status, first-generation status, work and living situation, plus standard demographics — for researchers and institutions studying access and equity. An AI follow-up interview digs into the real barriers behind first-generation and working-student status instead of just checking a box.

Measures how informed and involved parents feel in their child's education, what channels and activities actually reach them, and what stands in the way of deeper involvement. Built for school administrators and PTA/family-engagement leads, with an AI follow-up that reconstructs a specific recent interaction to surface the real reason engagement stalls.

Measures employee perceptions of productivity, well-being, collaboration, and engagement across remote, hybrid, and in-office arrangements. Use this instrument to identify burnout risk factors and inform flexible-work policy decisions.

A structured instrument for expert reviewers to evaluate the clarity, completeness, and pre-specification quality of a pre-analysis plan or preregistration draft before data collection begins.

Measures educators' AI tool usage patterns, institutional policy clarity, observed classroom outcomes, and support needs to inform evidence-based AI integration strategies across K-12 and higher education contexts.

Captures how students genuinely experience a course or program — clarity of teaching, fairness, workload, and engagement — paired with an AI follow-up that digs into the specific moment behind their overall rating. Built for instructors, program leads, and academic affairs teams who want more than end-of-term star ratings.

Measures adjunct and part-time instructor satisfaction across pay equity, institutional support, teaching resources, and scheduling predictability. Designed for higher education administrators seeking actionable data to improve adjunct working conditions.

Diagnoses why participants miss or consider skipping follow-up time points in longitudinal research, and identifies actionable levers—reminders, incentives, burden reduction—to improve retention and compliance.

Captures how a recent round of parent-teacher conferences went from the teacher's side — scheduling, resources, admin support, and the toughest conversations — with an AI follow-up that digs into one specific difficult conference to surface what actually got in the way. Built for department chairs and administrators reviewing conference logistics after each cycle.

부정행위가 실제로 얼마나 흔한지, 학생들이 어떤 행동을 용인할 만하다고 여기는지, 그리고 무엇이 실제로 편법을 쓰기로 하는 결정을 이끄는지를 측정합니다 — 학문적 정직성 문화를 점검하는 학교와 학과를 위한 것입니다. AI 후속 인터뷰는 죄책감을 유발하는 예/아니오 답변에 그치지 않고 특정 사건 이면의 사고 과정을 재구성합니다.

Measures how comfortable, trusting, and price-sensitive parents are about AI tutoring tools for their kids — covering current usage, top concerns, feature priorities, and willingness to pay. An AI follow-up interview digs into the specific worry or hesitation behind each parent's answers, surfacing what would actually change their mind.

학술 연구 등록에 앞서 잠재적 연구 참여자의 적격성을 선별하고 사전 동의 요소에 대한 이해를 확인합니다.

Captures how learners experienced an online course — content clarity, pacing, instructor delivery, and completion barriers — with an AI follow-up interview that digs into the specific moment a learner struggled, disengaged, or nearly dropped out. Built for course creators, instructional designers, and academic teams reviewing a cohort or module.

Measures how engaged, supported, and likely to stay teachers feel — covering workload, recognition, resources, and leadership support — for school and district leaders tracking staff morale. An AI follow-up interview digs into the specific reasons behind each teacher's engagement score and their top priority for improvement, surfacing detail no rating scale alone can capture.

An academic research survey examining how college and university students perceive and use AI writing tools such as ChatGPT, Copilot, and similar technologies. The survey covers usage patterns, perceived benefits and dependency concerns, academic integrity perspectives, quality comparisons, and instructor communication about AI policies.

Measures how students of all genders experience fairness, safety, and belonging at school — from classroom treatment to extracurricular access — with an AI follow-up interview that reconstructs a specific incident behind the numbers instead of leaving it as a vague rating.