classroomio/prd/ai-course-assistant

AI Course Assistant

Purpose

Deliver an AI assistant inside the course UI that teachers can use to:

• Upload a document (PDF/DOCX) and have the AI propose a full course structure
• Review a course plan with sections, lessons, and short descriptions before any content is created
• Execute the plan — the agent creates sections, lessons, and populates content automatically
• Update content: "Rewrite the introduction paragraph to be more engaging"
• Generate questions: "Given this lesson, come up with 5 MCQ questions"
• Draft lessons: Ask the agent to write the initial draft of a lesson

The assistant has two modes:

• Plan Mode — AI analyzes input (document or prompt) and proposes a course structure. The teacher reviews, edits, and approves before anything is created.
• Agent Mode — AI executes approved actions: creating sections, lessons, exercises, and populating content. Shows real-time progress.

Goals

• Help teachers go from a source document to a fully structured course in minutes
• Operate strictly within the current org/course context (no cross-org access)
• Integrate natively with existing ClassroomIO services (sections, lessons, exercises, content)
• Give teachers full control — always review before destructive or bulk actions

Non-Goals (v1)

• Student-facing AI (teachers/admins only)
• Cross-course or cross-org operations
• Custom model selection or fine-tuning
• Real-time collaboration (multi-user editing via agent)
• Internet/web search from the agent

---

Architecture Overview

Execution Model: Hybrid (Client Context + API Writes)

The client already has course content loaded in Svelte stores when the teacher is on a page. The agent:

• Reads context from the client — current lesson text, exercise questions, course structure are passed with each message. No extra API calls to "read" content.
• Writes through the API — all mutations route through existing service functions with the full middleware stack (authMiddleware -> orgMemberMiddleware -> courseTeamMemberMiddleware). Permission enforcement, single source of truth, and auditability for free.
• Previews before bulk changes — Plan Mode shows the full proposed structure before creating anything. For individual edits, the agent presents changes for review before applying.

Agent Architecture: Tool-Calling Pattern

The agent uses LLM function/tool calling:

Teacher -> Chat UI -> Agent API -> LLM -> tool_call -> Existing Services -> result -> LLM -> streamed response -> Chat UI

The LLM sees a set of tools (mapped to existing service functions) and decides which to call based on the teacher's message. The agent API executes the tool with full permission checks, then feeds the result back to the LLM for a human-friendly summary.

SDK: Vercel AI SDK

• ai — core (streamText, tool, types) — in packages/ai-assistant and apps/api
• @ai-sdk/svelte — Svelte 5 Chat class (runes-based, matches project's LessonApi / BaseApiWithErrors pattern)
• @ai-sdk/openai + @ai-sdk/anthropic — provider packages
• streamText() returns toUIMessageStreamResponse() which works directly as a Hono response
• Tool calling is built-in with Zod schemas; SDK handles function-calling protocol, multi-step execution, streaming tool results
• Provider abstraction lets us swap between OpenAI and Anthropic by changing one line

---

Plan Mode & Agent Mode

The two modes represent different phases of a workflow, not a toggle. A typical flow:

Plan Mode

Trigger: Teacher uploads a document or asks the agent to design a course structure.

What happens:

1. Teacher uploads a PDF/DOCX (or pastes text, or describes a topic)
2. Agent parses the document, extracts text content
3. Agent analyzes the content and generates a course plan — a structured proposal:

{
  "title": "Introduction to Biology",
  "sections": [
    {
      "title": "Cell Biology",
      "order": 1,
      "lessons": [
        {
          "title": "Cell Structure and Function",
          "description": "Overview of cell organelles, their roles, and how they work together",
          "order": 1,
          "hasExercise": true
        },
        {
          "title": "Cell Division: Mitosis and Meiosis",
          "description": "The processes of cell division, their stages, and biological significance",
          "order": 2,
          "hasExercise": true
        }
      ]
    }
  ]
}

4. The chat UI renders this as a structured plan view — a tree with sections and lessons, each showing title and description
5. Teacher can:
   • Approve as-is: "Looks good, implement it"
   • Request changes: "Add a section on genetics" / "Merge the first two lessons" / "Remove the exercise from lesson 3"
   • Edit inline: Modify titles/descriptions directly in the plan view
   • Reject: "Start over with a different structure"
6. The agent refines the plan based on feedback (still in Plan Mode)
7. Once approved, transitions to Agent Mode

Key constraint: Plan Mode is purely conversational. Nothing is created in the database until the teacher explicitly approves.

Agent Mode

Trigger: Teacher approves a plan, or asks the agent to perform a specific action (create lesson, update content, generate questions).

What happens for plan execution:

1. Agent receives the approved plan
2. Creates sections first (in order), capturing the returned section IDs
3. Creates lessons within each section (in order), capturing lesson IDs
4. For each lesson, generates full content based on the source document + lesson description, then calls update_lesson_content to populate it
5. Optionally creates exercises with generated questions for lessons marked hasExercise
6. Shows real-time progress via tool status cards in the chat:
   • "Creating section: Cell Biology..." -> checkmark
   • "Creating lesson: Cell Structure and Function..." -> checkmark
   • "Writing lesson content..." -> checkmark
   • "Creating exercise with 5 questions..." -> checkmark
7. After completion, onFinish callback refreshes the course content tree

What happens for individual actions (update lesson, generate questions, etc.):

Same as the standard agent flow — LLM decides which tool to call, executes it, streams the result.

Progress tracking: During plan execution, the agent emits structured progress events that the UI renders as a checklist:

[x] Section 1: Cell Biology (created)
[x]   Lesson 1: Cell Structure and Function (created)
[x]   Lesson 1: Content written (2,400 words)
[ ]   Lesson 1: Exercise (5 questions) — in progress...
[ ]   Lesson 2: Cell Division
[ ] Section 2: Genetics

Mode Detection

The mode is implicit based on context, not explicitly toggled by the user:

• If the agent is analyzing a document or proposing structure -> Plan Mode behavior (present plan, wait for approval)
• If the agent is executing a tool or the user said "implement it" -> Agent Mode behavior (execute and report)
• Regular chat (explain something, answer a question about the course) -> neither mode, just conversation

The system prompt instructs the LLM on when to propose vs. when to execute.

---

Document Upload & Parsing

Plan Gating: EARLY_ADOPTER+ Only

Document upload is a premium feature — only available to orgs on the EARLY_ADOPTER plan or higher. BASIC (free) plan users cannot upload documents.

Client-side behavior:

• The attachment icon in the chat input is visible to all teachers (so they discover the feature)
• When a BASIC plan user clicks it, the file picker does NOT open. Instead, the upgrade modal is triggered immediately (same openUpgradeModal() pattern used by certificates, team invites, etc.)
• For EARLY_ADOPTER+ users, the file picker opens normally

Server-side enforcement:

• POST /agent/upload checks the org's plan before processing
• If the org is on the BASIC plan, the endpoint returns 403 { error: "document_upload_requires_upgrade", upgradeRequired: true }
• The client handles this 403 by calling openUpgradeModal()
• This double-gating (client + server) prevents bypassing the restriction

File Size Limit

Maximum upload size: 5MB (aligns with the existing MAX_DOCUMENT_SIZE constant in apps/api/src/constants/upload.ts).

• Server validates file size before parsing. Files over 5MB are rejected with 413 { error: "file_too_large", maxSize: 5242880 }
• Client validates before upload as well (check file.size on selection) and shows an inline error: "File is too large. Maximum size is 5MB."
• 5MB is sufficient for most textbook chapters, syllabi, and course outlines. Full textbook PDFs (50MB+) are out of scope — teachers should upload individual chapters.

Upload Flow

1. Teacher clicks the attachment icon in the chat input
2. Plan check: if BASIC plan, trigger upgrade modal and stop. If EARLY_ADOPTER+, continue.
3. Selects a PDF or DOCX file
4. Size check: if file > 5MB, show inline error and stop
5. File is uploaded to POST /agent/upload which:
   • Validates plan (EARLY_ADOPTER+ required)
   • Validates file type (PDF/DOCX only) and size (max 5MB)
   • Extracts text content server-side
   • Returns { documentId, fileName, pageCount, textPreview } (first ~200 chars)
   • Stores extracted text in Redis with a TTL (keyed by documentId, expires after 1 hour)
6. The documentId is attached to the next chat message
7. When the agent processes the message, it retrieves the full extracted text from Redis and includes it in the LLM context

Text Extraction

PDF: Use pdf-parse (lightweight, pure JS, no native dependencies). Extracts text content from all pages. Handles multi-column layouts reasonably well for structured content like textbooks/syllabi.

DOCX: Use mammoth (pure JS). Extracts text content preserving heading structure (H1, H2, etc.) which helps the LLM understand document hierarchy for course structure generation.

Why Server-Side Extraction

• Keeps document parsing out of the client bundle
• Text is extracted once and cached in Redis — subsequent agent turns reference the same extracted text without re-uploading
• The LLM never sees the raw binary file — only clean text, which is more token-efficient
• File validation and size limits are enforced server-side

Upload API

POST /agent/upload
  Content-Type: multipart/form-data
  Body: file (PDF or DOCX, max 5MB)
  Auth: authMiddleware + orgMemberMiddleware + courseTeamMemberMiddleware
  Plan: EARLY_ADOPTER+ required (BASIC returns 403)

  Success Response (200): {
    documentId: string,
    fileName: string,
    mimeType: string,
    pageCount: number | null,    // PDF only
    wordCount: number,
    textPreview: string           // first ~200 chars
  }

  Error Responses:
    403: { error: "document_upload_requires_upgrade", upgradeRequired: true }  // BASIC plan
    413: { error: "file_too_large", maxSize: 5242880 }                         // > 5MB
    415: { error: "unsupported_file_type", allowed: ["application/pdf", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"] }

The extracted text is NOT returned in the upload response (it can be very large). It lives in Redis and is fetched internally by the agent chat endpoint when documentId is provided in the chat message.

Redis Key

agent:document:{documentId} -> { text, fileName, mimeType, uploadedAt }
TTL: 3600 seconds (1 hour)

Uses the existing Redis infrastructure (apps/api/src/utils/redis/).

---

Package: packages/ai-assistant

A thin, backend-focused package consumed by apps/api. No UI code. The heavy lifting is done by the Vercel AI SDK; this package provides project-specific configuration on top.

Responsibilities

• Provider factory — creates the right AI SDK LanguageModel from env vars (AI_PROVIDER, AI_API_KEY, AI_MODEL)
• Role-aware tool registry — getToolSchemas(role) returns Zod schemas per role
• Role-aware system prompt — buildSystemPrompt(context) generates different LLM instructions for teachers vs students, including Plan Mode vs Agent Mode behavior
• Course plan types — CoursePlan, CoursePlanSection, CoursePlanLesson types used by both API and dashboard
• Shared types — AgentRole, AgentContext, tool names, config types

Structure

packages/ai-assistant/
  src/
    index.ts                 # Public exports
    types.ts                 # AgentRole, AgentContext, CoursePlan, AIProviderConfig
    providers/
      index.ts               # createModel(config) factory
    tools/
      index.ts               # getToolSchemas(role) registry
      shared.ts              # Read-only tool schemas (both roles)
      teacher.ts             # Mutation tool schemas (teacher only, v1)
      student.ts             # Learning tool schemas (student only, v2 stubs)
    prompt/
      system-prompt.ts       # buildSystemPrompt(context) — role-aware
      teacher.ts             # Teacher prompt template (includes Plan/Agent mode instructions)
      student.ts             # Student prompt template (v2 stub)
  package.json
  tsconfig.json

The agent loop itself is NOT in this package — streamText() from ai core handles the full loop (prompt -> LLM -> tool call -> execute -> respond -> repeat). The Hono route assembles tools (schema from package + execute from services) and calls streamText.

---

Foundation Layer: Role-Aware Design

AgentRole — the core discriminator

• 'teacher' — can read AND mutate course content (create sections, update lessons, generate questions, execute plans)
• 'student' — can only read and discuss content (explain concepts, answer questions, quiz on material). Gated off in v1.

Role detection — server-side, automatic

• API route checks user's course membership via existing isCourseTeamMemberOrOrgAdmin
• Team members (ADMIN/TUTOR role in groupmember) get role: 'teacher'
• Regular members (STUDENT role) get role: 'student'
• In v1, role: 'student' returns 403

Tool registry — getToolSchemas(role)

• shared tools (read-only): available to both roles
• teacher tools (mutations): teacher only — includes section/lesson creation, content updates, plan execution
• student tools (v2): student only, stubs for now

Teachers get shared + teacher. Students get shared + student. Students can never access mutation tools, even via prompt injection.

Single API endpoint

/agent/chat serves both roles. Role is detected server-side (not passed by the client, preventing spoofing). The endpoint loads tools and prompt based on the detected role.

---

Agent Tools

V1 Core Tools (9 tools)

Tool	Type	Description	Service
get_course_structure	read	Get sections, lessons, exercises tree	getCourseContentItems + listCourseSections
get_lesson_content	read	Get a specific lesson's content (HTML)	getLesson + lesson language query
get_exercise_details	read	Get exercise with all questions and options	getExercise
create_section	write	Create a new section in the course	createCourseSection(courseId, { title, order })
create_lesson	write	Create a new lesson in a section	createLesson(courseId, { title, sectionId, order })
update_lesson_content	write	Update lesson text content	upsertLessonLanguageService(lessonId, { locale, content })
create_exercise	write	Create exercise with questions and options	createExercise({ courseId, lessonId, sectionId, title, questions })
add_questions	write	Add questions to an existing exercise	updateExerciseService (diff-based)
generate_course_plan	agent	Analyze text and return structured CoursePlan JSON	No service call — LLM generates the plan structure

Note on generate_course_plan: This is a special tool. Unlike other tools that call services, this tool's "execution" is the LLM itself generating a structured JSON response conforming to the CoursePlan schema. The tool schema defines the output format so the LLM returns valid, parseable JSON rather than free-form text. The chat UI detects this tool's result and renders the plan view component.

Phase 2+ Tools

Lessons: update_lesson_metadata, delete_lesson, reorder_lessons Sections: update_section, reorder_sections, delete_section Exercises: update_exercise_metadata, update_question, delete_question, delete_exercise Other: update_course_tags, create_newsfeed_post

Phase 4 Student Tools (v2)

explain_concept, quiz_me, clarify_exercise, get_my_progress, get_my_submissions

Tool Execution Rules

Each tool handler in the API will:

1. Extract IDs from tool arguments
2. Verify the target resource belongs to the course/org in context
3. Verify user is course team member or org admin
4. Check token balance before LLM call (see Token Usage section)
5. Call the existing service function
6. Return structured result to the agent

No-Hallucination Boundary

The system prompt includes an explicit list of what the agent CANNOT do. The agent must never:

• Pretend to perform an action it doesn't have a tool for
• Fabricate data it hasn't retrieved via a tool call
• Claim to have made a change unless a tool call succeeded
• Create content without presenting it for review first (in Plan Mode)

Explicit CANNOT-do list (included verbatim in system prompt):

• Cannot create, delete, or clone courses
• Cannot update course-level settings (title, description, logo, status)
• Cannot manage course members, invitations, or roles
• Cannot grade submissions or assign marks
• Cannot handle payments or attendance
• Cannot upload files, images, videos, or documents to lessons (the agent can receive documents for analysis, but cannot attach media to course content)
• Cannot delete sections, lessons, or exercises (v1 — added in Phase 2)
• Cannot reorder content (v1 — added in Phase 2)
• Cannot manage org settings, members, or billing
• Cannot access data from other courses or organizations
• Cannot browse the internet or fetch external URLs
• Cannot send emails or notifications

---

Token-Based Usage Tracking

AI usage is metered by tokens. Each org gets a monthly base allowance from their plan, with the option to buy extra credit packs when exhausted.

How It Works

1. Before LLM call: check ai_token_usage for current month + ai_credit_balance. If remaining <= 0, return 402.
2. After LLM call: streamText() result exposes usage with promptTokens and completionTokens. Insert row into ai_token_usage.
3. Deduction logic: monthly allowance consumed first. Credits only touched when used_this_month > allowance.

Plan-Based Allowances

Tied to existing organization_plan table. Each plan gets a monthly token allowance and feature access:

Plan	Token Allowance	Chat	Document Upload
BASIC	0 tokens	Not available	Not available — triggers upgrade modal
EARLY_ADOPTER	500K tokens/month (~50-100 conversations)	Available	Available (max 5MB per file)
ENTERPRISE	2M tokens/month (or unlimited)	Available	Available (max 5MB per file)

Allowance resets on the 1st of each month.

Document upload is gated by plan, not by token balance. Even if an EARLY_ADOPTER has tokens remaining, a BASIC user still cannot upload. This is a feature gate, not a usage gate.

Extra Credit Packs

• One-time purchases, do NOT expire, do NOT reset monthly
• Deduction order: monthly allowance first, then credits
• Purchase flow: org settings -> payment -> credits added to ai_credit_balance

Database Tables

CREATE TABLE ai_token_usage (
  id            BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  org_id        UUID NOT NULL REFERENCES organization(id),
  user_id       UUID NOT NULL,
  course_id     UUID NOT NULL,
  prompt_tokens INTEGER NOT NULL,
  completion_tokens INTEGER NOT NULL,
  total_tokens  INTEGER GENERATED ALWAYS AS (prompt_tokens + completion_tokens) STORED,
  model         TEXT NOT NULL,
  created_at    TIMESTAMPTZ NOT NULL DEFAULT now()
);
CREATE INDEX idx_ai_token_usage_org_month ON ai_token_usage (org_id, created_at);

CREATE TABLE ai_credit_balance (
  id            BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  org_id        UUID NOT NULL REFERENCES organization(id) UNIQUE,
  balance       INTEGER NOT NULL DEFAULT 0,
  updated_at    TIMESTAMPTZ NOT NULL DEFAULT now()
);

---

Safeguards

Four layers:

1. Route-level middleware — authMiddleware + orgMemberMiddleware + courseMemberMiddleware. Role detection inside handler.
2. Tool-level permission checks — tool set IS the security boundary. Students cannot call mutation tools. Each tool's execute function re-verifies resource ownership.
3. Token budget enforcement — every LLM call checks remaining balance. 402 if exhausted.
4. Rate limiting — per-user, per-org rate limits (requests/minute). Self-hosted is uncapped.

---

Self-Hosted vs Cloud

• Cloud: AI assistant enabled by default. ClassroomIO provides the LLM API key server-side. Usage metered per plan tier.
• Self-hosted: AI assistant disabled by default. To enable, admin sets AI_API_KEY and optionally AI_PROVIDER / AI_MODEL in env. When configured, assistant becomes available. When not configured, button is hidden. No token metering for self-hosted (user's own API key, their cost).

---

UI Specification

Trigger

"AI Assistant" button in the course sidebar navigation. In v1, only visible to teachers/admins. Hidden when self-hosted and no AI key configured.

Panel

Right sidebar using @cio/ui Sheet (side="right", ~420px). Does NOT replace the left sidebar — both can be open simultaneously.

Chat Interface

• Message list with user/assistant turns
• Text input at bottom with send button and attachment icon (for PDF/DOCX upload)
• Streaming token display (ChatGPT-like typing)
• Quick-action chips (context-dependent)
• Stop button while streaming

File Upload in Chat

• Attachment icon next to the text input (paperclip icon)
• BASIC plan users: clicking the attachment icon triggers the upgrade modal (openUpgradeModal()). File picker does not open. A subtle tooltip or label can hint "Upgrade to upload documents".
• EARLY_ADOPTER+ users: clicking opens file picker filtered to .pdf, .docx
• Client-side size check: if selected file exceeds 5MB, show inline error below the input: "File is too large. Maximum size is 5MB." File is not uploaded.
• Selected file shows as a chip above the input: [Biology-Textbook.pdf (24 pages)] [x]
• On send, file is uploaded first (POST /agent/upload), then documentId is included in the chat message
• While uploading: chip shows spinner. While parsing: "Analyzing document..."
• If server returns 403 (document_upload_requires_upgrade): trigger upgrade modal
• After parsing: agent enters Plan Mode and proposes a course structure

Plan View Component

When the agent returns a generate_course_plan tool result, the chat renders a plan card instead of plain text:

+------------------------------------------+
| Course Plan                              |
|                                          |
| Section 1: Cell Biology                  |
|   1. Cell Structure and Function         |
|      Overview of cell organelles...      |
|   2. Cell Division: Mitosis & Meiosis    |
|      The processes of cell division...   |
|                                          |
| Section 2: Genetics                      |
|   3. DNA and Gene Expression             |
|      Structure of DNA, transcription...  |
|   4. Heredity and Genetic Variation      |
|      Mendelian genetics, Punnett...      |
|                                          |
| [Implement Plan]  [Ask for Changes]      |
+------------------------------------------+

• Sections are collapsible
• Each lesson shows title + short description
• "Implement Plan" button -> triggers Agent Mode execution
• "Ask for Changes" button -> focuses the text input with a prompt to describe changes
• Teacher can also just type feedback naturally: "Add a section on ecology"

Agent Mode Progress

During plan execution, a progress card replaces the plan card:

+------------------------------------------+
| Implementing Course Plan                 |
|                                          |
| [x] Section: Cell Biology                |
|   [x] Lesson: Cell Structure             |
|   [x] Content written (1,850 words)      |
|   [x] Exercise: 5 MCQ questions          |
|   [~] Lesson: Cell Division              |
|   [ ] Content                            |
|   [ ] Exercise                           |
| [ ] Section: Genetics                    |
|   [ ] Lesson: DNA and Gene Expression    |
|   [ ] Lesson: Heredity                   |
|                                          |
| Creating lesson content...               |
| [Stop]                                   |
+------------------------------------------+

• Checkmarks for completed steps
• Spinner for in-progress step
• Empty circles for pending steps
• Stop button to halt execution (already-created content is kept)

Role-Aware Quick Actions

Teacher (v1):

• "Upload a document to create a course"
• "Draft a lesson"
• "Generate questions from this lesson"
• "Improve this lesson's text"

Student (v2):

• "Explain this lesson"
• "Quiz me on this topic"
• "Help me with this exercise"

Context Awareness

Client passes courseId, current lessonId or exerciseId. The role is NOT sent from the client — detected server-side. The system prompt includes: "The teacher is currently viewing lesson/exercise ID: {id}".

Store Refresh

onFinish callback fires after the agent completes. Refreshes the relevant store:

• After plan execution: full course content tree refresh
• After lesson update: lessonApi.get() reload
• After exercise creation/update: exercise data reload

---

Streaming

The Vercel AI SDK provides structured SSE via toUIMessageStreamResponse():

• Text chunks (tokens stream one by one)
• Tool call events (when LLM decides to call a tool)
• Tool results (when tool execution completes)
• Finish signals

On the frontend, the Chat class from @ai-sdk/svelte consumes this protocol automatically. No manual SSE parsing.

---

API Routes

New route domain: /agent

Method	Route	Description	Auth
GET	/agent/status	Feature flag + role + usage info	auth + orgMember
POST	/agent/chat	Main chat endpoint (SSE stream)	auth + orgMember + courseMember
POST	/agent/upload	Upload PDF/DOCX for parsing	auth + orgMember + courseTeamMember
GET	/agent/usage	Detailed usage stats	auth + orgMember
POST	/agent/credits	Purchase credits	auth + orgAdmin

POST /agent/chat

{
  courseId: string,
  messages: UIMessage[],      // Vercel AI SDK message format
  context?: {
    lessonId?: string,
    exerciseId?: string,
    documentId?: string        // References uploaded document in Redis
  }
}

Returns: SSE stream (toUIMessageStreamResponse())

Flow:

1. Detect role from course membership
2. Check token balance
3. If documentId provided, fetch extracted text from Redis and inject into context
4. Load tools + system prompt based on role
5. Call streamText() with maxSteps: 10 (plan execution may need more steps than simple queries)
6. Stream response to client
7. Record token usage after completion

POST /agent/upload

// Request: multipart/form-data with 'file' field
// Response:
{
  documentId: string,
  fileName: string,
  mimeType: string,
  pageCount: number | null,
  wordCount: number,
  textPreview: string
}

Registered in apps/api/src/app.ts as .route('/agent', agentRouter).

---

LLM Provider Abstraction

Configurable via environment variables:

• AI_PROVIDER — openai | anthropic (default: openai)
• AI_API_KEY — the provider API key
• AI_MODEL — model name override (default: provider's recommended model)

Provider factory in packages/ai-assistant normalizes into a single LanguageModel instance for streamText().

---

Conversation Persistence

v1: Session-scoped — managed in-memory by the Chat class on the client. Conversation resets when sidebar closes or teacher navigates away.

v2 consideration: Persist conversations per course in the database.

---

Example Flows

Flow 1: Upload Document -> Plan -> Execute

The core differentiating flow. Teacher has a textbook PDF and wants to turn it into a course.

1. Teacher opens AI Assistant, clicks attachment icon, selects Biology-101.pdf
2. File uploads to /agent/upload. Chat shows: [Biology-101.pdf (142 pages)]
3. Teacher sends: "Create a course structure from this textbook"
4. Agent receives the message + extracted text from Redis
5. Plan Mode: Agent analyzes the document structure (chapters, headings, key topics) and calls generate_course_plan tool, returning a structured CoursePlan
6. Chat renders the plan card with sections and lessons
7. Teacher reviews: "Looks good but split the genetics section into two — basic and advanced"
8. Agent refines the plan, returns updated CoursePlan
9. Teacher clicks "Implement Plan"
10. Agent Mode: Agent executes the plan step by step:
    • create_section("Cell Biology", 1) -> sectionId
    • create_lesson(courseId, { title: "Cell Structure", sectionId, order: 1 }) -> lessonId
    • update_lesson_content(lessonId, { locale: "en", content: "<generated HTML>" }) -> success
    • create_exercise({ courseId, lessonId, sectionId, title: "Cell Structure Quiz", questions: [...] }) -> exerciseId
    • ... repeat for each lesson in each section
11. Progress card updates in real-time
12. On completion: "Course structure created with 4 sections and 12 lessons. Content has been populated for all lessons."
13. Course content tree refreshes — teacher sees the full course

Flow 2: "Generate 5 MCQ questions from this lesson"

Teacher is viewing a lesson and opens the AI assistant.

1. Teacher sends: "Generate 5 multiple choice questions from this lesson"
2. Context includes lessonId
3. LLM calls get_lesson_content(lessonId) to read the lesson text
4. LLM generates 5 questions
5. LLM calls create_exercise({ courseId, lessonId, sectionId, title, questions }) or add_questions(exerciseId, questions) if an exercise already exists
6. Chat shows tool status cards + summary
7. Exercise data refreshes

Flow 3: "Rewrite the introduction paragraph"

1. Teacher sends: "Rewrite the introduction paragraph to be more engaging"
2. Context includes lessonId
3. LLM calls get_lesson_content(lessonId)
4. LLM identifies the introduction, rewrites it
5. LLM presents the new introduction in chat and asks for confirmation
6. Teacher says "apply it"
7. LLM calls update_lesson_content(lessonId, fullUpdatedContent) — replaces full lesson HTML with only the introduction changed
8. Lesson store refreshes

Flow 4: "Design a course about machine learning" (no document)

Plan Mode works without a document too — the teacher can describe what they want.

1. Teacher sends: "Design a 6-week course about machine learning for beginners"
2. Agent enters Plan Mode using its training knowledge (no document needed)
3. Returns a CoursePlan with sections and lessons
4. Teacher reviews and approves
5. Agent Mode executes — creates sections, lessons, writes content from LLM knowledge

---

Implementation Phases

Phase 1: Foundation

• Create packages/ai-assistant with types (AgentRole, AgentContext, CoursePlan), provider factory, tool registry (9 v1 tools), system prompt with Plan/Agent mode instructions and no-hallucination boundary
• Add ai_token_usage and ai_credit_balance tables to DB schema
• Add GET /agent/status and POST /agent/chat route skeleton with role detection (students get 403 in v1) and token balance check
• Add POST /agent/upload route with PDF/DOCX parsing (pdf-parse, mammoth)
• Add Redis key management for uploaded document text
• Add "AI Assistant" button to course sidebar (gated on status endpoint + plan check)
• Build right sidebar Sheet with basic chat UI (hardcoded echo responses)
• Add file upload UI (attachment icon, file chip, upload progress)

Phase 2: Agent Core

• Implement provider factory (OpenAI + Anthropic) using createModel(config)
• Wire 9 v1 tools to existing services via streamText() with maxSteps: 10
• Implement generate_course_plan tool with CoursePlan JSON schema
• Record token usage after each call; enforce balance check before each call
• Connect chat UI to streaming API via @ai-sdk/svelte Chat class
• Render streamed tokens and tool status cards
• Build Plan View component (structured plan card with sections/lessons)
• Build Agent Mode progress card (checklist with real-time updates)
• Implement "Implement Plan" button -> plan execution flow
• Add usage meter to AI assistant sidebar header
• Implement onFinish store refresh for course content tree, lessons, exercises

Phase 3: Polish + Safeguards + Credits

• Tool-level permission re-validation (verify resource belongs to course/org)
• Rate limiting (per-user, per-org, requests/minute)
• Quick-action chips and context-aware suggestions
• Self-host env var detection and GET /agent/status logic
• Conversation context windowing (trim long histories, especially after document upload)
• POST /agent/credits for purchasing credit packs
• Credit purchase UI in org settings + exhausted-state UI in chat
• GET /agent/usage for detailed usage dashboard
• Stop button during plan execution (graceful — keeps already-created content)

Phase 4: More Tools

• Section management: update_section, reorder_sections, delete_section
• Lesson management: update_lesson_metadata, delete_lesson, reorder_lessons
• Exercise management: update_exercise_metadata, update_question, delete_question, delete_exercise
• Other: update_course_tags, create_newsfeed_post

Phase 5: Student Assistant (v2)

• Student tool schemas and execute functions
• Student system prompt template
• Remove the v1 student 403 gate
• Student quick-action chips
• Surface AI assistant in the student course view (course-app)
• Student usage counts against org token budget (same pool)

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New Dependencies

Package	Where	Purpose
ai	packages/ai-assistant, apps/api	Vercel AI SDK core (streamText, tool, types)
@ai-sdk/svelte	apps/dashboard	Svelte 5 Chat class for streaming chat
@ai-sdk/openai	packages/ai-assistant	OpenAI provider
@ai-sdk/anthropic	packages/ai-assistant	Anthropic provider
pdf-parse	apps/api	PDF text extraction
mammoth	apps/api	DOCX text extraction

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Files to Create / Modify

New files

• packages/ai-assistant/ — entire new package (types, providers, tools, prompts)
• apps/api/src/routes/agent/ — agent route handlers (chat, upload, status, usage, credits)
• apps/api/src/services/agent/ — agent service (document parsing, token tracking)
• apps/dashboard/src/lib/features/ai-assistant/ — UI components (sheet, chat, plan view, progress card, file upload)

Modified files

• packages/db/src/schema.ts — add ai_token_usage and ai_credit_balance tables
• apps/api/src/app.ts — register /agent route
• apps/api/src/utils/redis/key-generators.ts — add agent document keys
• apps/dashboard/src/lib/features/course/components/sidebar/course-sidebar-navigation.svelte — add AI Assistant button

Existing files referenced (services the agent will call)

• apps/api/src/services/course/section.ts — createCourseSection, listCourseSections
• apps/api/src/services/lesson/lesson.ts — createLesson, getLesson
• apps/api/src/services/lesson-language.ts — upsertLessonLanguageService
• apps/api/src/services/exercise/exercise.ts — createExercise, updateExerciseService
• packages/db/src/queries/course/content.ts — getCourseContentItems

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Success Criteria

• Teacher can upload a PDF/DOCX and get a proposed course structure within 30 seconds
• Teacher can review, edit, and approve the plan before any content is created
• Agent can execute an approved plan end-to-end: creating sections, lessons, and content
• Teacher can see real-time progress during plan execution
• All standard agent actions work: update lesson text, generate questions, draft lessons
• No user can modify another org's data; all actions pass permission checks
• Self-hosted instances can enable/disable the assistant via env var
• Token usage is tracked and metered per org plan