교육·학술

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 teacher-reported reliability, error frequency, and satisfaction with LMS integrations and single sign-on (SSO) systems. Use this instrument to identify failure points, prioritize fixes, and benchmark integration performance over 30-day recall windows.

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.

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.

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.

교사가 학교에서 지원, 자원, 업무량, 리더십을 어떻게 경험하는지를 만족도 평가, 우선순위 지정 활동, 예산 배분 트레이드오프를 통해 파악합니다. AI 후속 질문은 일반적인 불만 대신 가장 낮게 평가된 영역 뒤에 있는 구체적인 사건을 깊이 있게 다룹니다. 직원 분위기 설문을 진행하는 학교 관리자와 인사팀을 위해 설계되었습니다.

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.

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.

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.

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.

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.

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 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 student satisfaction, perceived quality, and outcomes after academic advising sessions. Designed for institutional advising offices seeking actionable feedback to improve service delivery.

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.

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 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.

학업 진실성 정책에 대한 학생의 인식, 부정행위와 관련된 또래 규범 인식, 기관 차원의 개입에 대한 견해를 측정합니다. 진실성 문화를 강화하기 위한 실행 가능한 데이터를 찾는 교육 관리자와 연구자를 위해 설계되었습니다.

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.

Measures the quality of institutional internship support, supervision quality, and skill development outcomes to guide program improvement for higher education administrators and career services teams.