We're running a research program with partner schools and academic collaborators to test — rigorously — what self-paced AI learning does for mastery, equity, teacher wellbeing, and family outcomes. Studies are listed below; results are pending.
Compares AI-customized vs. generic quizzes with 20 students using an AB/BA crossover design. Measures weekly affect (PANAS-C + Smileyometer) alongside accuracy and time-on-task, analyzed via mixed-effects models. Investigates whether personalization of question content meaningfully shifts how students feel about quizzes — not only how they perform on them.
Compares student engagement, affect, and free-response sentiment when learners are permitted to skip lessons in subjects they have already demonstrated mastery of, versus proceeding through full sequencing. Twenty students complete an AB/BA crossover. Measures weekly affect (PANAS-C + Smileyometer), behavioral engagement (time-on-task, voluntary practice, lesson-completion rate), and NLP-based semantic and sentiment analysis of weekly student journal entries; analyzed via mixed-effects models.
nonstatic.education runs an in-house applied research function with our pilot partner schools. We pre-register study protocols where possible, use validated instruments (PANAS-C, Smileyometer) alongside platform telemetry, and commit to publishing null and negative results alongside positive ones. Studies marked in progress are actively collecting data; pending studies are in design or awaiting cohort start.