Grounded in Science,
Powered by Evidence.
We don't believe in "black box" education. EduAdapt.ai is built on decades of cognitive science research and transparent, explainable AI methodologies.
Pedagogical Foundations
Our algorithms aren't just optimizing for engagement—they are optimizing for retention and mastery based on how the human brain actually learns.
Cognitive Science Principles
We incorporate Spaced Repetition and Interleaved Practice directly into the learning path. This ensures knowledge moves from working memory to long-term storage, combating the 'forgetting curve.'
Mastery Learning
Inspired by Bloom's 2 Sigma Problem, we ensure students don't move forward until they grasp the prerequisite concepts. This prevents the accumulation of 'learning gaps' that hinder future success.
Evidence-Based Assessment
Our diagnostics are designed to measure understanding, not just recall. We utilize Item Response Theory (IRT) to accurately calibrate the difficulty of questions to the learner's ability level.
The Feedback Loop
Our models don't just teach; they learn. We constantly refine our content and recommendations based on aggregate learning data.
Diagnose
Identify learner strengths, gaps, and misconceptions.
Adapt
Generate a personalized path and scaffolded content.
Deliver
Present targeted practice and immediate feedback.
Refine
Measure outcomes to update the learner model.
Bias Reduction & Fairness
Algorithmic bias is a critical risk in EdTech. We actively monitor our models to ensure they do not penalize students based on dialect, cultural context, or background.
- Regular algorithmic fairness audits
- Diverse training datasets
- Explainable AI (XAI) decision logic
Continuous Evaluation
We don't just measure prediction accuracy; we measure educational impact.
Read Our Whitepapers
Dive deeper into the technical architecture and the learning science behind EduAdapt.