AI in Education: Adaptive Learning for Students

EdTech AI faces three hard problems: content is expensive to create (courses, assessments, explanations all need expert authorship); personalisation requires understanding learner state (what they know, what they're confused about, how they learn best); and student data requires FERPA/COPPA-compliant handling. Traditional e-learning platforms bolt on 'AI' features as afterthoughts: recommendation engines that recommend the next video in a list. Real adaptive learning requires a fundamentally different architecture.

SpeedMVPs builds adaptive EdTech MVPs using a modular LLM architecture: an LLM-based content generator creates explanations, hints, and practice questions dynamically based on learner state; a knowledge graph models the curriculum as a dependency tree so the system always knows what prerequisite gaps need addressing; a spaced repetition engine schedules review sessions at optimal intervals; and a teacher/admin dashboard provides visibility into class-wide learning patterns. We deploy in 2–3 weeks with FERPA-compliant data handling.