Curriculum-Grounded Learning Assistant

An education provider wants to support learners with different needs while educators handle repetitive questions, lesson preparation, assessments, and progress reporting. Generic chatbots can give unsupported answers and uniform learning paths may not reflect learner progress.

• Course content changes by subject, term, and instructor version.
• Learners need explanations without improperly completing restricted assessments.
• Student data can be sensitive, particularly for minors.
• Accessibility, language needs, cost, and educator trust affect adoption.

Use retrieval-augmented generation over approved course material. Cite sources, offer hints and step-by-step explanations, and summarize recurring questions for educators. Teachers retain control over content and assessment policy.

• Ingest approved course documents, objectives, and instructor FAQs.
• Index content with course, module, version, and permission metadata.
• Retrieve relevant passages before generating answers and show citations.
• Provide hints rather than reveal restricted assessment answers.
• Let educators review, edit, publish, or remove content.
• Escalate low-confidence or out-of-scope questions.

Evaluate answers against instructor-reviewed questions, citation support, unsupported-answer rate, and consistency across learner levels. Test accessibility and usability. Measure learning outcomes only with an appropriate comparison design; usage alone does not prove learning improved.

• Educator time spent on repetitive questions.
• Answer correctness and source-support rate.
• Escalation rate and satisfaction.
• Learning outcomes versus a suitable baseline, if measured.