Adapt to the mind,
not just the syllabus.
Stop guessing why students churn. nx10 turns raw interaction kinematics into real-time cognitive insights, allowing your platform to dynamically adjust pacing, prevent burnout, and protect learning streaks.
The Blind Spot
The myth of the completion rate.
Educational apps constantly battle cognitive overload. A user breaking their 100-day streak isn't always a failure of willpower; it's a failure of the software to recognize cognitive depletion.
The Flaw in Static Pacing
Currently, learning apps balance progression using spaced repetition algorithms or quiz scores. If a user fails a module three times, the system assumes they don't know the material.
But why did they fail? Are they genuinely struggling with the concept, or are they just exhausted after a 10-hour workday? Treating a cognitively depleted user the same as an energized user guarantees frustration and eventual churn.
The Nx10 Solution
nx10 analyses the kinematics of the learner's inputs. We measure how precision and reaction timing degrade over the course of a session to calculate their remaining mental stamina.
The Large Feelings Model (LFM) provides real-time access to the Brain Charge Index (BCI), allowing your curriculum engine to know exactly when to push the student, and when to let them rest.
The Platform Ecosystem
Empowering every discipline.
Learning Designers
Goal: Maximise knowledge retention without burning out the user.
Use the Brain Charge Index to validate curriculum pacing. Prove mathematically that breaking a 10-minute lesson into three 3-minute chunks reduces cognitive fatigue and increases next-day recall.
Product Managers
Goal: Protect the Daily Active Streak.
A broken streak is the #1 leading indicator of app uninstall. Intervene the moment the Red/Green Frustration index spikes during a hard module, offering a "Save Streak" lifeline before they rage-quit.
Data Scientists
Goal: Build hyper-personalized learning algorithms.
Stop treating all users identically. Group learners into "Affective Cohorts" based on their kinematic baseline (e.g., "Anxious Learners" vs "Confident Grinders") and feed these vectors into your recommendation models.
The Playbooks
From science to systems.
How tier-1 educational platforms are utilizing nx10 SDKs and Server Webhooks to build highly adaptive curriculums.
Cognitive-Aware Pacing
If a user is tackling a difficult new language concept or a complex math problem, their cognitive load will inevitably increase. The trick is knowing exactly when to let them rest so they don't associate your app with exhaustion.
The Implementation:
By monitoring the Brain Charge Index (BCI) via the iOS/Unity SDK, the client app detects when the user's cognitive state drops into .depleted. Instead of serving the next complex module, the app seamlessly injects a dopamine-heavy, low-friction "Review Mini-Game." It waits until the BCI trend reads .increasing before presenting new, difficult material.
func determineNextModule() {
let bciState = Nx10.getCurrentBCI()
if bciState.level == .depleted {
// Brain juice is empty. Prevent burnout.
queueModule(type: .easyReviewGame)
Analytics.log("Pacing_Slowed")
} else {
// User has capacity for new concepts
queueModule(type: .newConcept)
}
}
async function generateCurriculum(userId) {
// Fetch user's longitudinal LFM embedding
const profile = await nx10.getAffectiveCohort(userId);
if (profile.phenotype === 'High_Anxiety_Learner') {
// Apply high-reassurance learning track
return curriculumService.buildTrack({
pacing: 'slow',
positiveReinforcement: 'high'
});
}
return curriculumService.buildStandardTrack();
}
Affective Cohort Routing
Different users react to the exact same UI in entirely different ways. A timer counting down might induce a fun "Flow State" in a competitive user, but induce debilitating panic in an anxious learner.
The Implementation:
By querying the Nx10 Insights API from your backend, you can identify a user's fundamental Affective Phenotype. If a user consistently registers high physiological stress during timed exercises, your backend can automatically route them to an untimed, exploration-based learning track - saving the user and maximizing their LTV.
Ready to enhance the learning experience?
Get your API keys and deploy emotional analytics today.
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