教育・学術向けアンケートテンプレート

Captures how graduating students rate their academic experience, campus support, and readiness for what's next — from advising and coursework to job offers or grad school plans. Built for registrars, career centers, and academic affairs teams tracking outcomes across cohorts and programs. An AI follow-up interview digs into the specific moments that shaped a student's sense of readiness, going beyond a single rating.

Captures how student-athletes experience your high school sports program — coaching, scheduling, facilities, and how well athletics balances with academics — for athletic directors and coaches reviewing a season. An AI follow-up interview digs into the story behind satisfaction and recommendation scores instead of stopping at a number.

Measures how clearly parents understand what they're agreeing to when they sign school consent forms — for field trips, media releases, research studies, or medical care — and how much they trust the process. An AI follow-up interview digs into the specific confusion or hesitation behind any low-confidence answer instead of leaving it as a number.

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.

Captures how students experience a teacher's clarity, fairness, feedback, and engagement using a rating battery and a best-worst trade-off on what matters most, then uses an AI follow-up interview to dig into the specific moment behind their lowest rating instead of a vague complaint. Built for course or semester-end teacher evaluations.

Measures students' sense of belonging, perceived fairness and safety, and firsthand experiences with exclusionary or discriminatory treatment on campus. Built for institutional research and DEI offices, with an AI follow-up interview that reconstructs the specific incident or context behind a student's belonging score instead of leaving it as an abstract number.

Evaluates how comprehensive, comfortable, and useful a sex education program felt to students or recent graduates — covering which topics were actually taught, how comfortable people felt asking questions, and where they turned when information was missing. An AI follow-up interview reconstructs a specific moment where the curriculum fell short, going deeper than a topic checklist.

Measures how common cheating really is, which behaviors students consider acceptable, and what actually drives the decision to cut corners — for schools and departments auditing their academic integrity culture. An AI follow-up interview reconstructs the reasoning behind a specific incident instead of settling for a guilt-laden yes/no.

Measures how satisfied parents are with their child's school across communication, safety, academics, and responsiveness — with an AI follow-up that digs into the specific moment or interaction driving each parent's overall recommendation score. Built for school administrators and PTA leaders who want more than a satisfaction number.

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.

Gathers student feedback on a teacher's clarity, fairness, engagement, and support, plus which qualities matter most to learning. An AI follow-up interview digs into the specific moment behind each student's overall recommendation score, surfacing concrete examples instead of vague praise or complaints.

Captures how students experienced a course — clarity of instruction, workload, materials, and instructor effectiveness — plus a best-worst prioritization of what to fix first. An AI follow-up interview digs into the reasoning behind the overall rating so instructors get specific, actionable detail instead of a single number.

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.

Measures which topics, formats, and learning activities genuinely capture student curiosity versus which ones fall flat, for instructors and instructional designers refining a course or program. An AI follow-up interview digs into the specific moment interest sparked or died, surfacing detail that ratings alone can't reveal.

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 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.

Captures how smooth (or frustrating) the school registration process was for parents and guardians — from completing forms to submitting documents — with an AI follow-up that digs into the single biggest obstacle families hit. Built for school offices and district enrollment teams who want to fix friction before the next enrollment cycle.

Measures how well a training class, private trainer, or app actually changed a pet's behavior — covering goals worked on, progress made, and satisfaction with instruction. An AI follow-up interview digs into the specific moment behavior clicked (or didn't) and what got in the way, for trainers and pet-training platforms who want more than a star rating.

Tracks how satisfied students are with teaching, advising, facilities, and campus life, and pinpoints which improvements matter most using a best-worst trade-off exercise. An AI follow-up interview digs into the specific moment or experience behind each student's satisfaction score, surfacing detail closed questions miss.

Collects updated contact and career details from graduates re-registering with your alumni network, along with their interest in mentoring, events, and giving. An AI follow-up interview digs into what would genuinely motivate them to reconnect, rather than just which boxes they check.

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.

Measures how smoothly students and families experienced the return to school — communication before the first day, logistics, emotional readiness, and stress points — for districts, schools, or PTAs planning next year's transition. An AI follow-up interview reconstructs the specific moment things went well or fell apart, beyond a satisfaction score.

Measures how satisfied students are with teaching quality, course content, workload, support services, and campus facilities, and pinpoints which areas most urgently need improvement — with an AI follow-up that digs into the specific class or interaction behind their overall satisfaction score.

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.

Measures how safe, respected, and connected students feel day-to-day — covering bullying exposure, fairness of discipline, and adult support — with an AI follow-up interview that digs into the real story behind students' lowest-rated experience instead of just the number they picked.

Measures how effectively an AI tutor supports student comprehension, engagement, and skill growth, for educators and edtech teams evaluating tutoring tools, with an AI follow-up interview that reconstructs a recent tutoring session to surface what helped or confused the student.

For nursing, medical, and allied health programs using VR simulation modules. Measures perceived realism, skill and confidence transfer, and how VR compares to traditional training methods — with an AI follow-up that reconstructs a specific moment where the simulation helped or failed to prepare a learner for real clinical practice.

Measures whether AI lesson-planning and grading tools are actually reducing teacher workload, which tasks see the biggest time savings, and how much teachers trust the output. Built for school and district leaders evaluating AI tool adoption, with an AI follow-up interview that reconstructs a real week to separate genuine time savings from hidden re-work.

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.

Measures whether an AI skills training program actually changed how employees work — confidence gains, specific skill areas, and real on-the-job use — for L&D teams evaluating a completed cohort. The AI follow-up interview digs into what participants actually did differently at work and what's blocking wider adoption.

Measures student stress levels, workload, sleep habits, and use of mental health support, then identifies the biggest barriers keeping students from asking for help. Built for student affairs, wellness centers, and academic advisors; the AI follow-up reconstructs a specific recent moment a student needed support but didn't seek it, and what would have changed that.

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.

Measures how confident parents feel about their child's safety, how well the center communicates day-to-day, how warm staff feel toward kids, and whether developmental activities feel age-appropriate. An AI follow-up digs into the specific incident or gap behind any low safety or communication score, surfacing details a rating alone would miss.

Measure behaviors people won't admit directly: the list experiment (item count technique) asks only HOW MANY statements apply — never which — so individual answers stay genuinely deniable while group comparisons reveal the true rate. The native question type randomizes control and treatment lists for you.

A complete consent flow for research studies: plain-language study information, granular consent statements with a comprehension check, signature capture, and contact enrollment. Built for IRB-style requirements — participants confirm they understand, not just that they scrolled.

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.

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.

A research instrument measuring how generative AI tools affect student learning outcomes across learning depth, critical thinking, knowledge retention, and metacognitive awareness. Designed for pre/post and experimental research designs. Estimated completion time: 10–14 minutes.

This survey evaluates student experiences with AI chatbot advising and support services. It measures usage patterns, task types, perceived success rates, satisfaction, trust, comparison to human advisors, and areas for improvement.

This survey assesses instructor preparedness to integrate artificial intelligence into teaching, covering technology proficiency, AI literacy, pedagogical confidence, institutional support, professional development needs, and workload concerns. Estimated completion time: 10 minutes.

A research survey examining how AI tools are reshaping academic integrity norms among college and university students. Covers policy awareness, personal usage patterns, acceptability perceptions, peer behavior observations, and policy recommendations.

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.

An academic instrument measuring student awareness of, exposure to, and attitudes toward artificial intelligence ethics. Covers concept familiarity, training exposure, ethical dilemma responses, regulation views, and willingness to prioritize ethics over convenience. Estimated completion: 8–12 minutes.

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 effectively employees apply recent training to their jobs, identifying barriers, enablers, and perceived outcomes to improve learning transfer and training ROI.

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.

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.

Assesses how well current curriculum, resources, and support structures meet learner needs, identifying gaps and priorities to guide program improvements. Designed for enrolled students or active learners in formal or informal educational settings.

Measures research participants' preferences for study session length, break scheduling, and compensation structures. Designed for academic and UX researchers optimizing study design for participant comfort and data quality.

A bi-weekly pulse survey measuring student engagement behaviors, support resource usage, and perceived learning progress. Designed for course instructors and instructional designers seeking actionable insights to improve eLearning and hybrid course experiences.