Education & Academic

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.

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.