教育・学術向けアンケートテンプレート

A general-purpose Likert-scale instrument skeleton for graduate dissertation research, pairing validated-style attitude, norm, and intention batteries with an AI follow-up that probes the reasoning behind attitude-behavior gaps. Built for thesis researchers to adapt with their own constructs; demographics are collected last for clean subgroup analysis.

Captures how much parents budget and spend across back-to-school categories — clothing, supplies, and technology — and how that spending pressures household finances. An AI follow-up digs into the trade-offs behind a parent's stress score: what got delayed, financed, or cut to cover the school list.

Captures how prepared, heard, and informed parents felt during a recent parent-teacher conference, what topics were actually covered, and whether a clear next-steps plan resulted — with an AI follow-up that reconstructs the specific concerns raised and whether they were resolved. Built for schools and PTAs auditing conference quality.

Captures why families register for your Vacation Bible School, their scheduling and logistics preferences, and any health or accessibility needs — with an AI follow-up that surfaces the real motivations and hesitations behind a family's choice instead of just checkbox answers.

Measures how likely students are to recommend a course, program, or instructor to a peer, what drives that willingness, and where the experience falls short. An AI follow-up interview digs into the specific reasons behind each student's score, surfacing concrete moments and details that a rating alone can't capture. Built for academic departments, bootcamps, and course teams tracking word-of-mouth and retention risk.

Captures how learners actually experienced an online training course — completion, content quality, instructor clarity, and confidence applying the material — for L&D teams and course designers. An AI follow-up interview digs into the real obstacles behind low satisfaction or recommendation scores instead of settling for a bare number.

Screens teaching candidates on certification, subject and grade-level fit, classroom experience, and job priorities before an in-person interview. Built for school and district hiring teams; the AI follow-up interview goes past rehearsed talking points to reconstruct how a candidate actually handled a real classroom challenge.

Measures whether diversity training changes real workplace behavior, not just satisfaction — covering relevance, confidence applying concepts, and inclusion climate. An AI follow-up interview digs into a specific moment participants applied (or struggled to apply) what they learned, for L&D and DEI teams evaluating training impact.

A short weekly pulse check for students learning remotely — covering engagement, workload, tech reliability, and connection to instructors. An AI follow-up interview digs into whatever obstacle the student flagged (tech, motivation, unclear instructions) to find out exactly what happened and what would help, instead of stopping at a checkbox.

Measures how thoroughly and consistently school bus drivers complete pretrip inspections, what gets in the way, and how confident they are in catching defects before a route — with an AI follow-up that reconstructs a real recent inspection instead of relying on general self-reports. Built for transportation directors and safety managers auditing inspection culture.

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.

Captures how fans, media, or panel voters actually build a college football Top 25 ballot — which teams they'd rank where, which criteria drive those calls, and how confident they are in the official rankings. The AI follow-up interview digs into the reasoning behind a voter's #1 pick and any team where their ballot diverges sharply from the published poll.

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.

Captures how a training session landed — content quality, trainer effectiveness, and confidence applying new skills — for L&D teams and instructors. An AI follow-up interview digs into the real story behind the recommendation score: what specifically helped or blocked people from using what they learned.

Captures how students rate a teammate's contribution, communication, reliability, and work quality on a shared project, plus how they'd split credit across the team. An AI follow-up interview digs into the specific behavior behind the rating instead of settling for a vague number, giving instructors evidence to catch free-riders and recognize strong collaborators.

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.

A new-client intake for personal trainers and coaches that captures training goals, current fitness level, injury/medical history, scheduling needs, and consistency barriers before the first session. An AI follow-up interview digs into the real motivation and biggest obstacle behind the client's stated goal so the trainer can program around it from day one.

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.

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.

Measures students' intent to re-enroll, sense of belonging, and satisfaction with academic, financial, and mental-health support, then uses an AI follow-up interview to surface the real story behind low re-enrollment intent or a student weighing whether to leave. Built for student affairs, advising, and retention teams who need to spot risk before it becomes attrition.

Gathers feedback from parents and guardians whose children participate in a nonprofit's educational program — covering communication, perceived impact on the child, satisfaction, and likelihood to continue — with an AI follow-up that digs into the specific moment or interaction behind their impact rating.

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.

Evaluates whether a train-the-trainer program actually builds facilitation skill and gets used back on the job — covering content relevance, confidence delivering sessions, and skill transfer. An AI follow-up interview reconstructs a real training session the respondent led afterward, not just their impression of the course.

Captures how school students are experiencing remote or online classes — focus, workload, technology access, and connection with teachers and classmates — with an AI follow-up that digs into the single biggest obstacle they name. Built for school administrators, ed-tech teams, and researchers evaluating distance learning programs.

Captures how well remote or hybrid learning is working for your students and families — engagement, workload, tech reliability, and communication — with an AI follow-up that digs into the single biggest obstacle parents are facing at home.

Measures how students experienced a social studies course — material relevance, teaching clarity, discussion quality, and which units actually landed — for teachers, department heads, and curriculum teams reviewing a course after a term. An AI follow-up interview digs into the reasoning behind students' recommend score and topic preferences instead of stopping at the number.

Tracks how connected alumni of a nonprofit program (fellowship, service corps, leadership cohort, scholarship, etc.) still feel to the organization, what keeps them involved or drifting away, and which reengagement offerings they'd actually value — with an AI follow-up that digs into the real reasons behind their current engagement level.

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 safe students feel at school, how often they witness or experience bullying and violence, and how much they trust adults to respond — with an AI follow-up interview that reconstructs a specific safety incident and probes what would make a student more likely to report one. Built for middle and high school safety climate assessments.

Captures how teachers are actually experiencing remote and hybrid instruction — platform use, engagement and assessment challenges, confidence levels, and support priorities — for school and district leaders planning training and tools. An AI follow-up interview digs into each teacher's single biggest obstacle and what would genuinely fix it, not just a wish list.

Evaluates how well a training program delivered on content quality, instructor effectiveness, and real-world applicability, including a best-worst trade-off on what matters most in future sessions. An AI follow-up interview digs into whether participants have actually used the skills on the job and what got in the way if not. Built for L&D teams and training providers assessing course impact.

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.

Captures how students research schools, manage the application workload, and weigh factors like cost, location, and program strength — for high school counseling offices, admissions consultants, and ed-tech tools. An AI follow-up interview digs into the single biggest obstacle each student hit during the process.

Measures how smoothly students moved through course registration — from system usability to getting into desired classes — for registrars, advising offices, and student success teams. An AI follow-up interview digs into the specific moment registration broke down for each student, surfacing fixable friction that a satisfaction score alone can't explain.

Measures what path graduating students are choosing after high school (college, work, military, trade, gap year), how confident they feel, and who influenced the decision — for counselors, schools, and districts. An AI follow-up interview digs into the specific uncertainty or concern behind each student's plan.

Captures how residents, students, and local business owners experience neighborhood change linked to growing student populations — rents, displacement, local business shifts, and town-gown relations — with an AI follow-up that digs into specific, lived examples behind the numbers instead of vague impressions.

Gathers feedback from students who acted, sang, danced, ran crew, or played in the pit for their school's musical — covering rehearsal workload, mentorship, teamwork, and personal growth. An AI follow-up interview digs into one specific challenging moment from the production, giving directors and drama teachers concrete detail behind the ratings.

Measures how satisfied students are with instruction, advising, facilities, and support services this term, and what the college should prioritize improving next. An AI follow-up interview digs into the story behind each student's recommendation score, surfacing specific incidents and unmet needs that closed-ended ratings miss. Built for academic affairs and student experience teams.

Captures how teachers experience support, resources, workload, and leadership at their school — using satisfaction ratings, a prioritization exercise, and a budget trade-off — with an AI follow-up that digs into the specific incident behind their lowest-rated area instead of generic complaints. Built for school administrators and HR teams running staff climate surveys.

Measures how much sleep students actually get on school nights, what disrupts it, and how tiredness shows up in the classroom — with an AI follow-up that reconstructs a specific bad night to surface the real cause instead of a vague 'I stayed up late.' Built for school counselors, researchers, and administrators studying student wellbeing.

Measures how satisfied Pre-K through 12th grade parents are with their child's school across communication, safety, academic rigor, and teacher responsiveness, and shows what matters most to them when priorities compete. An AI follow-up interview digs into the specific experience behind their overall rating instead of just the number, for school leaders and PTA/district research teams.

Measures how well a school's contact with parents actually works — which channels get used, how quickly urgent matters get followed up, and whether messages get read at all. The AI follow-up reconstructs one specific recent contact experience in detail, surfacing breakdowns that satisfaction scores alone would miss.

Captures how families and students experienced remote and hybrid learning during the coronavirus pandemic — engagement, workload, tech access, and school communication — for education researchers and administrators. An AI follow-up interview digs into the single biggest obstacle a respondent flags, reconstructing what actually happened on a typical remote-learning day.

Checks whether employees actually understood mandatory compliance and legal training — harassment, data privacy, conflicts of interest, and reporting duties — through scenario-based questions, then uses an AI follow-up interview to probe the reasoning behind the shakiest answer. Built for L&D and legal teams validating training effectiveness before sign-off.

Measures how confident and supported parents feel helping their child with schoolwork, which school resources they actually use, and where the biggest gaps are. Includes an AI follow-up interview that reconstructs a real recent moment of struggle to surface what support would have actually helped. Built for schools and PTAs planning parent-engagement programs.

Maps how parents and caregivers actually divide day-to-day responsibilities for a child's education — homework help, school communication, events, and decisions — and where the load feels unbalanced. Built for schools, family researchers, and co-parenting programs, with an AI follow-up that digs into the most recent moment the division felt unfair or broke down.

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.

Checks how well new hires actually absorbed onboarding training — knowledge-check questions on your policies, safety, and systems content, plus confidence ratings across modules. An AI follow-up interview digs into whichever topic the new hire flags as least clear, surfacing concrete gaps a scored quiz alone would miss. For L&D and onboarding teams; takes about 6 minutes.

Captures how families experienced your school's inquiry-to-decision journey — from first contact through tours, interviews, and the admissions decision — with an AI follow-up that digs into the single moment that most shaped their impression of the school.

Gathers student feedback on a teacher's instructional clarity, fairness, availability, and classroom management, then uses an AI follow-up interview to unpack a specific moment behind the overall rating. Built for department chairs, instructional coaches, and school administrators running end-of-term teacher evaluations.