Course Materials to Queryable Knowledge Base
A course knowledge base turns files, assignments, and notes into searchable objects with source context.
Evidence, retrieval, agents, and the paths that connect a question to a grounded answer.
Interactive model
Queryable course context
The same retrieval mechanics can power chat, flashcards, wikis, quizzes, and weekly plans.
Live HTML simulation · adjust the controls and watch the computed output respond.
Interactive
Hybrid retrieval turns a vague study question into ranked evidence
This is a simplified teaching model. Its displayed values are computed from the controls; the article explains where the model stops.
Site connection
Lykke ingests Canvas materials and turns them into AI-generated wikis, flashcards, quizzes, and study workflows.
From File Pile to Knowledge Layer
A course distributes knowledge across PDFs, Canvas pages, assignments, announcements, slides, grades, and calendar events. A queryable knowledge base converts those materials into addressable records while preserving where each record came from.
Lykke's source describes Canvas ingestion through OAuth and a Chrome extension, then embedding and indexing materials so chat, wikis, flashcards, quizzes, infographics, and weekly plans can reuse the same course context.
Mental model: the knowledge base is a library catalog plus retrievable excerpts, not one giant textbook. The analogy stops when extraction loses layout, formulas, or relationships that a catalog record cannot reconstruct.
An Ingestion Contract
Each connector should produce a stable source identity, content, source type, course scope, timestamps, and permissions. Parsing then converts the source into text and structure; chunking creates coherent retrievable units; embeddings and lexical terms make those units searchable.
Idempotent ingestion avoids creating duplicates when Canvas content is synchronized again. A content hash or source version can distinguish an unchanged file from a revision and support selective re-indexing.
| Field | Why it matters | Example |
|---|---|---|
| Source identity | Trace and update the record | Canvas assignment ID |
| Course scope | Prevent cross-course retrieval | CS 210 |
| Content type | Interpret and rank correctly | Rubric, lecture, announcement |
| Freshness | Prefer current information | Updated timestamp or version |
| Permissions | Restrict who may retrieve it | Enrolled student access |
Metadata Is Not Optional
Course, week, unit, due date, source file, content type, and version all help filtering, ranking, citation, and refresh. Without metadata, a search system cannot reliably distinguish a policy from a definition or an active deadline from an old announcement.
Metadata is evidence about context, not a substitute for content. It can exclude correct passages when wrong or missing, so ingestion tests should compare metadata against the source system.
Worked Example
Suppose a professor edits Project 2 in Canvas, moving the deadline and adding a rubric criterion. The connector sees the same source identity with a new modification time or content hash, fetches the revision, parses it, and replaces or versions affected chunks.
A student then asks, 'What is due Friday, and what should I review first?' Metadata retrieval locates current deadlines and course scope; semantic retrieval finds lecture concepts used by the project; a reranker prioritizes the revised assignment and relevant lecture chunks.
The answer should label deadline claims as coming from the Canvas assignment and conceptual guidance as coming from lecture material. If the source provides no recommended order, the system may clearly mark an inferred study sequence rather than attributing it to the professor.
One Knowledge Layer, Several Study Views
Chat, flashcards, wiki pages, quizzes, infographics, and weekly plans are different transformations of the same grounded records. Reusing a common retrieval layer keeps source updates and access rules consistent while allowing each artifact to apply a different teaching structure.
Artifact generation should record source identities and source versions. When a source changes, the system can mark dependent artifacts stale, regenerate them, or preserve the old version for comparison instead of silently presenting outdated content.
Limits and Common Misconceptions
A knowledge base is not automatically complete or correct. OCR, table extraction, duplicate uploads, missing permissions, stale synchronization, and malformed metadata can all corrupt the searchable layer.
Queryable does not mean answerable: some questions require material never supplied, and some course rules are ambiguous across sources. Retrieval should surface conflicts and insufficiency rather than merge them into false certainty.
More metadata and smaller chunks are not always better. Excessive fragmentation can separate a definition from its conditions, while over-specific filters can hide relevant evidence.
Common Pitfalls
- Treating all course files as one undifferentiated text blob.
- Losing source filenames during ingestion.
- Failing to re-index when Canvas content changes.