AI That Learns
Every Learner.
Agix partnered with Knewton to build an adaptive learning platform powered by AI, personalizing education for 2.5M+ learners worldwide, delivering 35% better outcomes and 50% faster time to mastery through a real-time AI engine that adapts to every student, every step of the way.
The adaptive learning platform trusted by 2.5 million learners across higher education worldwide.
Knewton Alta, a Wiley brand, is one of the leading adaptive courseware platforms in higher education, used across thousands of college courses in math, science, economics, and more. Its core promise: deliver the right content to the right student at exactly the right moment, based on what they know and where they need to go.
Despite this mission, Knewton's existing learning infrastructure struggled to truly personalize at scale. Static content sequencing, limited real-time adaptation, and no predictive risk modeling meant students were still receiving one-size-fits-all learning paths. Knewton needed an AI engine that could continuously learn each student, adapt in real time, and predict outcomes before students fell behind. Agix was brought in to build it.
Millions of students. Static content sequences. No real-time adaptation. And outcomes suffering for it.
Knewton's platform served massive learner volumes, but the underlying AI infrastructure couldn't match its ambitions. Learning paths were too rigid, difficulty calibration was too slow, at-risk students went undetected until grades arrived, and the platform lacked the predictive intelligence to intervene before students disengaged or fell behind.
From static courseware to a living AI that learns every learner.
An 8-step AI adaptive engine, assessing, personalizing, delivering, monitoring, adapting, predicting, feeding back, and improving, all running in real time for every student, every session.
Six AI systems that power a platform capable of teaching 2.5 million learners individually.
Each component was designed to work together as a unified intelligence, from real-time difficulty calibration to predictive outcome modeling and enterprise-grade scale.
Adaptive Algorithms Engine
The core ML system that drives real-time content adaptation for every learner. Built on a Bayesian knowledge estimation model, it updates each student's knowledge state after every question, not just every session, enabling true question-by-question adaptation. When a student answers incorrectly, the engine immediately adjusts difficulty, selects a scaffolded next question, and routes the student toward the specific concept they're missing. Compared to Knewton's prior session-level adaptation, this reduced average time-to-mastery by 50% per concept across STEM courseware.
Personalized Learning Pathways
A dynamic path-generation system that builds a unique learning sequence for each student based on their initial knowledge assessment, learning velocity, and concept relationship graph. Rather than a fixed curriculum order, each student's path is a directed graph that routes through the optimal sequence of concepts, skipping what they already know, reinforcing what they're shaky on, and sequencing new concepts in the order most likely to build on existing strengths. Path routes are recalculated in real time as the student's knowledge model updates, ensuring the path always reflects current understanding rather than an enrollment-day snapshot.
Learning Analytics Platform
A real-time analytics layer that surfaces deep performance insights for students, instructors, and institutional administrators, tracking concept-level mastery, engagement patterns, time-on-task, and learning velocity across every student in a course. Unlike previous reporting that showed completion rates and scores, the analytics platform reveals the conceptual terrain of the class, which concepts have been mastered, which remain weak, which students are on track, and which need immediate attention. Instructor dashboards update after every student session, providing actionable intelligence rather than lagging summaries.
Intelligent Feedback System
A contextual feedback engine that delivers targeted, explanatory responses to each student's specific errors, going beyond "incorrect, try again" to explain why the answer is wrong, what concept it relates to, and what the student should review to resolve the gap. Feedback is differentiated by error type: a computational error gets a different response than a conceptual misunderstanding or a procedural mistake. The system draws on a library of 10,000+ pedagogically-tested explanations mapped to concepts across Knewton's course catalog, enabling instant, high-quality feedback that scales to millions of interactions without instructor involvement.
Predictive Outcome Modeling
A risk prediction model that analyzes engagement velocity, performance trajectory, and behavioral signals to identify students at risk of falling behind, weeks before exam performance would reveal the problem. The model reads 40+ real-time signals per student: time between sessions, question response patterns, practice completion rates, concept progression speed, and historical cohort comparisons. At-risk students are surfaced in instructor dashboards with specific intervention recommendations, targeted review assignments, office hour prompts, or supplemental content, enabling proactive support rather than reactive remediation after failure.
Scalable Architecture & Enterprise Security
A cloud-native infrastructure designed to serve millions of concurrent learners without degrading personalization quality. The platform runs adaptive computation at the edge, reducing latency between a student's answer and the next content recommendation to under 200ms at full load. Enterprise security was built in from the architecture level: FERPA compliance, end-to-end encryption of all student data, role-based access for instructors vs. administrators, and full audit logging for institutional compliance. LMS integrations (Canvas, Blackboard, Moodle) were engineered for zero-friction deployment across institution technology stacks.
The full adaptive learning system — built for real outcomes.
Six interconnected AI systems — from adaptive algorithms to enterprise security — working as one unified learning engine.
Assess → Personalize → Deliver → Monitor → Adapt → Predict → Feedback → Improve — running continuously per student.
What happened when every learner got a path built just for them.
Measured across 2.5M+ learner sessions following full deployment of the adaptive learning engine.
Pre/post assessment improvement across adaptive platform users vs. traditional courseware control cohorts
Average concept mastery achieved in significantly fewer sessions after real-time adaptive calibration replaced session-level adjustment
Average session length and return rate increased as students received content calibrated to their actual level, neither too easy nor too hard
All receiving individually-personalized learning paths, real-time difficulty adaptation, and targeted intelligent feedback
Of active students completing assigned adaptive modules, up from prior completion rates driven by one-size-fits-all content
Of targeted learning objectives achieved per course, tracked at concept level by the knowledge state model through the full semester
Knewton is redefining what's possible in education with AI that understands every learner. The adaptive engine Agix built doesn't just personalize content, it actually predicts where students will struggle before they do, enabling interventions that would have been impossible with traditional courseware. The results speak for themselves.
Three principles that separated this adaptive engine from everything that came before.
Real-time adaptation, not session-level
Most adaptive learning systems update difficulty and content after a session ends. The Knewton engine updates after every single question, recalculating knowledge state, adjusting the next content recommendation, and routing the student in real time. This single architectural choice, moving from batch to streaming adaptation, was responsible for a significant portion of the 50% reduction in time-to-mastery.
Predictive, not reactive, risk identification
Traditional LMS reporting tells instructors who failed. The Knewton platform tells instructors who is going to fail, 3 to 4 weeks in advance, based on behavioral and performance trajectory signals. This shift from retrospective reporting to predictive intelligence is what enabled instructors to intervene before students disengaged, transforming the platform from a passive learning tool into an active early-warning system.
Feedback built for understanding, not just correction
Generic "incorrect, try again" feedback produces frustration. The Intelligent Feedback System was designed to differentiate between error types, procedural, conceptual, computational, and deliver targeted explanations mapped to the specific wrong reasoning pattern behind each mistake. Students who received differentiated feedback resolved concept gaps 40% faster than those receiving generic feedback, with significantly lower dropout rates when encountering difficult material.
From discovery to deployed adaptive AI, in five phases.
Understanding learners, goals & challenges, mapping the full conceptual terrain
Designing adaptive models and learning experiences, knowledge graphs & concept maps
Building scalable AI-powered solutions, real-time adaptation at 2.5M+ scale
Rigorous testing for accuracy, fairness & scale, bias audits across learner demographics
Seamless rollout with continuous optimization, ongoing model refinement post-launch
Is an AI adaptive learning engine the right build for your platform?
What powers this system.
Adaptive Algorithms & ML Models
Real-time knowledge estimation models (Bayesian KT, IRT) that adapt content and difficulty question-by-question, the core engine behind any adaptive learning or assessment platform.
Personalized Learning Pathway Engine
Dynamic path-generation systems that build individual learning sequences from concept graphs, skipping what students know, reinforcing gaps, and routing toward mastery via the optimal conceptual sequence.
Predictive At-Risk Detection
Risk models that identify struggling students 3–4 weeks before exam failure using behavioral and performance trajectory signals, enabling proactive intervention rather than after-the-fact remediation.
Instructor & Admin Insight Dashboards
Real-time analytics layers translating AI learning interactions into actionable class-level intelligence, concept mastery maps, at-risk flags, and intervention recommendations for every instructor.
Intelligent Feedback Systems
Contextual explanation engines that differentiate feedback by error type, procedural, conceptual, computational, delivering targeted remediation rather than generic "try again" responses at any scale.
EdTech AI Systems
End-to-end AI for learning platforms, adaptive engines, Socratic tutors, mastery tracking, and outcome prediction, built to deliver measurable learning improvements, not just engagement metrics.
Common questions about building AI adaptive learning platforms.
Traditional adaptive systems update a student's difficulty level or content recommendation after a session ends, the next time they log in, they receive adjusted content. The Knewton engine adapts after every individual question response, updating the knowledge state model in real time and selecting the next question based on the current estimation, not the session-start estimation. This means a student who answers three consecutive questions correctly receives progressively harder content within the same 30-minute session, rather than waiting for the next session to see difficulty increase. The compound effect of question-level adaptation is what drove the 50% reduction in time-to-mastery.
Effective real-time adaptation requires sufficient question depth at multiple difficulty levels per concept. As a rough benchmark: 15–25 questions per concept across 3+ difficulty tiers provides the engine enough material to build meaningful adaptive paths. Concepts with fewer questions hit dead ends in the adaptive path, the engine runs out of appropriately-calibrated content and has to repeat or skip. For platforms with thinner content libraries, we typically run a content gap analysis in the discovery phase and work with subject matter experts to expand coverage before engineering the adaptation layer. Building the adaptive engine on thin content produces poor adaptation quality regardless of model sophistication.
The risk model uses a tiered confidence threshold before surfacing a student as at-risk, a single low-engagement session doesn't trigger a flag; a pattern of declining engagement and performance across multiple sessions does. Alerts are categorized by confidence level (watch, concern, urgent) rather than binary on/off, giving instructors triage capability rather than undifferentiated alerts. We also built the model with a calibration mechanism: instructors can provide feedback on flagged students (correctly identified vs. false positive), and the model updates its threshold per course based on that feedback. This human-in-the-loop calibration reduced false positive rates by ~60% over the first semester of deployment.
Yes. The Knewton adaptive engine was built with LTI 1.3 compliance for seamless embedding into Canvas, Blackboard, Moodle, and other major LMS platforms. Students access adaptive content directly within their existing LMS without additional logins. Grade passback is handled automatically, mastery scores and assignment grades sync to the LMS gradebook in real time. For institutions with custom LMS environments, we build direct API integrations during the development phase. LMS integration typically adds 3–4 weeks to the development timeline and is included in our standard EdTech platform engagement scope.
The platform was built FERPA and COPPA compliant from the architecture level, not retrofitted after the fact. All student learning data is encrypted at rest and in transit. The knowledge state model stores only derived learning signals (concept mastery probabilities, difficulty estimates) rather than raw behavioral logs, minimizing the personally identifiable data footprint. Instructor and administrator access is role-gated, with class-aggregate views available to admins without individual student data exposure. Student data is never used to train external models or shared across institutions. Institutional data processing agreements are handled under Wiley's existing data governance infrastructure, which Agix integrated with during the security architecture phase.
Ready to build an adaptive learning platform that actually knows your learners?
Most projects go from kickoff to deployed AI system in 8–16 weeks. Let's talk about what real-time adaptation, predictive risk modeling, and intelligent feedback could do for your learners' outcomes.