교육·학술

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.

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 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 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.

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 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.

학생들이 등교하는 날 밤에 실제로 얼마나 자는지, 무엇이 수면을 방해하는지, 그리고 피로가 교실에서 어떻게 나타나는지를 측정합니다. 막연한 '늦게까지 깨어 있었다' 대신 실제 원인을 드러내기 위해 특정한 나쁜 밤을 재구성하는 AI 후속 질문이 포함되어 있습니다. 학생 웰빙을 연구하는 학교 상담교사, 연구자, 관리자를 위해 만들어졌습니다.

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 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.

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 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.