AI-assisted adaptive learning platform for ML/AI engineers — RAG-grounded explanations, competency progression, spaced repetition
Fixed course sequences delivered identically to every learner — no personalisation.
Replay how learner state, competency gaps, and spaced repetition determine the next learning action.
Curriculum path: Fixed sequence, identical for all learners
Curriculum path: Personalised adaptive path per placement result
Learners receive paths matched to their demonstrated competency with RAG-grounded explanations and targeted microdrills when mastery checks fail. Explicit XP mechanics (500 XP/level, 40 XP/microdrill, 50 XP/quest) create visible progress milestones. The target metric is a 68% improvement in quiz completion rates vs a fixed-path baseline — not yet measured at scale.
IRT CAT must converge on a reliable ability estimate in 15–25 items. Each item is selected to maximise Fisher information at the current θ estimate, making the diagnostic efficient. Learners who skip the diagnostic start at θ=0 (population mean) and the estimate updates rapidly from the first 5 interaction events.
some advanced competency nodes have few explicitly defined prerequisite edges, creating sequencing ambiguity. Resolved by inferring implicit co-prerequisites from learner performance patterns across the cohort: nodes that are consistently failed together are treated as implicit prerequisites.
initial GPT-4o quiz generation had a ~12% ambiguity or answer-leakage rate without a validation layer. A GPT-4o-mini judge with a structured rubric reduced this to ~9%. Persistent failures on the same objective fall back to a curated question pool.
the XP system required calibration so that one level (500 XP) corresponds to approximately 8–12 hours of engaged learning. Calibrated via content time estimates (microdrills ~4 min, quests ~25 min) and adjusted against a pilot cohort to match realistic learner pacing.
Course material is chunked into 1,200-character sentence-boundary units and embedded at 768 dimensions. At explanation time: the learner's detected misconception + learning objective is embedded, top-k 8 chunks retrieved from FAISS, passed to GPT-4o with a grounding requirement. The system prompt explicitly states that every claim must be supported by the retrieved context. Grounding rate: ~86% (RAGAS faithfulness, internal eval on 100 explanation samples — prototype benchmark).