AI educationFoundational

Course Materials to Queryable Knowledge Base

A course knowledge base turns files, assignments, and notes into searchable objects with source context.

LykkeKnowledge baseCanvasAI education

Site connection

Lykke ingests Canvas materials and turns them into AI-generated wikis, flashcards, quizzes, and study workflows.

Visual model

Queryable course context

The same retrieval mechanics can power chat, flashcards, wikis, quizzes, and weekly plans.

Interactive

Hybrid retrieval turns a vague study question into ranked evidence

Lecture: embeddingskeyword 0.34 / vector 0.94
0.87
Canvas calendar exportkeyword 0.76 / vector 0.42
0.79
Syllabus policieskeyword 0.38 / vector 0.62
0.72
Study guide draftkeyword 0.28 / vector 0.68
0.61

From File Pile to Knowledge Layer

A course usually stores knowledge in inconvenient places: PDFs, Canvas pages, assignments, announcements, slides, and calendar events.

The knowledge base normalizes those pieces into records that can be retrieved, cited, and regenerated into study artifacts.

Metadata Is Not Optional

Course, week, unit, due date, source file, and content type all help retrieval. Without metadata, the system cannot tell whether a chunk is a policy, a definition, or an assignment deadline.

Common Pitfalls

  • Treating all course files as one undifferentiated text blob.
  • Losing source filenames during ingestion.
  • Failing to re-index when Canvas content changes.

Quick check

Quiz

Why keep metadata with chunks?
  1. To improve retrieval and source context
  2. To make JSON longer
  3. To avoid embeddings
  4. To prevent search

Metadata helps filter, rank, cite, and update retrieved material.

Sources and Further Reading

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