Education solution
Adaptive Learning AI Systems
Knowledge-graph-guided instruction that diagnoses a learner's misconception, explains the solution, and selects the next question based on demonstrated mastery.
04 · Education
Adaptive learning in a working AI application.
Meet Maya Carter, a fictional learner. She chooses Derivatives: Marginal Analysis, answers a business-calculus question, and receives immediate feedback. If her answer is wrong, the AI professor identifies the misconception, works through the solution, and gives her a related question to confirm mastery.
- Named learner session
- Corrective feedback
- Knowledge-graph adaptation
A sample learning path
Meet Maya Carter.
- Name
- Maya Carter
- Topic
- Derivatives · Marginal analysis
QuestionIf C(q) = 500 + 12q + 0.02q², what is marginal cost at q = 100?
Maya's answer$14Not quite
Guided solution: Differentiate first: C′(q) = 12 + 0.04q. Then substitute q = 100, so marginal cost is $16 per additional unit.
Live interactive demonstration
Business Calculus Trainer
Try the fictional name Maya Carter. Choose Derivatives → Marginal Analysis, answer the prompt, and intentionally submit one incorrect response to see the guided solution and retry. Use sample information only.
Adaptive education
Turn a course structure into a responsive learning system.
Discuss the curriculum, knowledge graph, feedback standard, learner experience, evaluation plan, and human-review controls.
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