Build an AI-Powered Learning Assistant with Auto-Generated Diagrams and Examples

Why an AI-Powered Learning Assistant for Teaching Sites

Teaching websites thrive when concepts are explained clearly, reinforced with clean visuals, and demonstrated through examples. An AI learning assistant can automate this pipeline end-to-end, helping educators scale high-quality lessons with minimal manual effort.

What you gain:

Faster content production: generate explanations, diagrams, and examples in minutes.
Consistency: uniform styles across lessons and courses.
Personalization: adapt difficulty and style by learner profile.
Lower costs: leverage affordable diagram generator AI and efficient workflows.

We’ll build a practical stack that turns any topic into a guided exposition, structured diagrams, and visual outputs—ready to embed on course pages.

Architecture: Explain → Diagram → Visualize (LLM + Nano Banana)

At the core is a simple workflow:

Explain: Use an LLM to produce concise, structured explanations and illustrative examples.
Diagram: Convert explanation summaries into diagram prompts; generate images with Nano Banana models.
Visualize: Assemble lesson content, render base64 images, and optionally attach short videos for motion demos.

Components

LLM service: Generates plain-language explanations, step lists, and example Q&A.
Nano Banana image generation: Diagram generator AI for clean visuals from textual prompts.
Optional Sora AI video: Short scene videos to reinforce learning.
Storage/CDN: Persist base64 images and serve cached assets.
API gateway: Orchestrates requests, rate limits, and logs.
Frontend: Renders lessons, diagrams, accessibility alt text, and localized metadata.

Flow Diagram (conceptual)

Input: Topic + learner level → LLM explain → key points → diagram prompt → Nano Banana generate → base64 image → assemble lesson → publish.

Pricing, Performance, and When to Use Pro

Teams often choose Nano Banana for its cost and consistent latency:

Official grade output quality
Fast 10 secs generation
Stable performance under high volume

Pricing highlights (current):

Standard image: 0.02 USD per image (official rate 0.039 USD).
Nano Banana Pro: 0.068 USD per image (half the official 0.134 USD), for complex, higher-detail visuals.
Sora AI Video: 0.12 USD per video (vs. 1–1.5 USD official), suitable for short educational clips.

When to use Pro:

Dense diagrams (multi-layered network topologies, biochemical pathways).
Detailed labels, small text, or multi-panel figures.
Brand-critical visuals where precision is required.

API Design and Data Model

Design your assistant around a few endpoints:

POST /explain: Generate structured explanation + examples.
POST /diagram: Create one or more diagrams from points.
POST /lesson: Orchestrate explain → diagram → assemble, returns lesson payload.
POST /video (optional): Generate short reinforcing scenes.
GET /jobs/{id}: Check async status for diagrams/videos.

Suggested data model for a lesson (simplified):

lesson_id: string
topic: string
learner_profile: enum (beginner, intermediate, advanced)
explanation: markdown
key_points: array of strings
examples: array of { prompt, reasoning, solution }
diagrams: array of { id, alt_text, base64, caption, source_model }
video: { id, url or base64, caption, status }
metadata: { cost_estimate, durations, cache_key }

Implementation Steps

Step 1: Prompt Templates

Prompts are the backbone of consistent outputs. Use system and user templates.

LLM system template:

Role: Teaching assistant for [subject].
Objectives: Explain concisely, produce step-by-step reasoning, include at least one example.
Constraints: Avoid jargon when possible, define terms, keep sections short.
Output schema: key_points (5–8 bullets), explanation (markdown), examples (2–3), diagram_briefs (2–3 short prompts).

LLM user template:

Inputs: { topic, learner_level, prior_knowledge }
Instruction: Generate explanation, examples, and diagram_briefs. Prefer labeled structures (arrows, boxes). Avoid decorative elements.

Step 2: LLM Explain Service

Implement your explain endpoint so it returns:

key_points
explanation (markdown)
examples (with reasoning)
diagram_briefs (short, structured prompts)

Pseudo-request (you can adapt to your LLM provider):

POST /explain
{
  "topic": "Binary Search Trees",
  "learner_level": "beginner",
  "prior_knowledge": "arrays, sorting"
}

Response (conceptual):

{
  "key_points": [
    "BST stores ordered keys for fast lookup",
    "Left subtree < node < right subtree",
    "Operations: insert, search, delete",
    "Time complexity depends on balance"
  ],
  "explanation": "## What is a BST...",
  "examples": [
    {"prompt": "Search for 15 in BST", "reasoning": "Compare with root...", "solution": "Found at right subtree..."}
  ],
  "diagram_briefs": [
    "Labeled BST with arrows showing insert path",
    "Balanced vs. skewed BST side-by-side"
  ]
}

Step 3: Diagram Generation Workflows (Nano Banana)

Use Nano Banana as your diagram generator AI. Two model choices:

Standard: gemini-2.5-flash-image
Pro: gemini-3-pro-image-preview

Example request (images) via the chat completions endpoint with 10-second base64 returns:

curl --location --request POST "https://wisdom-gate.juheapi.com/v1/chat/completions" \
  --header "Authorization: Bearer YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --header "Accept: */*" \
  --data-raw '{
    "model": "gemini-2.5-flash-image",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Generate a clean, labeled BST diagram with nodes 10, 5, 15, showing insert arrows."}
      ]
    }],
    "stream": false
  }'

You can also include a reference image URL or base64 for style consistency:

{
  "model": "gemini-3-pro-image-preview",
  "messages": [{
    "role": "user",
    "content": [
      {"type": "text", "text": "Side-by-side balanced vs skewed BST, clear labels, arrow annotations."},
      {"type": "image_url/base64", "image_url": {"url": "https://blog-images.juhedata.cloud/9105_output_1794ff4b.jpeg"}}
    ]
  }]
}

Response will carry base64 image data; persist it and attach metadata:

source_model: gemini-2.5-flash-image or gemini-3-pro-image-preview
generation_time: ~10s
cost: 0.02 USD (standard) or 0.068 USD (Pro)

Mapping briefs to prompts:

Use structured language: “Top-down flowchart, boxes labeled ‘Input’, ‘Process’, ‘Output’, arrows with verb phrases.”
Prefer clarity over photo-realism; diagrams need readable labels and clean lines.

Step 4: Lesson Assembly and Rendering

Combine the explanation, examples, and diagrams into a single lesson payload:

Keep sections short with H2/H3 headings and bullets.
Add alt_text for accessibility, matching key points.
Caption the diagram with the learning objective (“Trace insert path”).
Provide example exercises underneath the diagram.

Rendering hints:

Lazy-load base64 images; swap to CDN URLs after persistence.
Ensure dark/light theme compatibility (adjust borders, label colors).
Include a “Regenerate diagram” button for learners who want variations.

Step 5: Optional Video Generation (Sora AI)

Short videos can reinforce dynamic processes (e.g., algorithm animations, physics phenomena).

Step 1: Make video

curl -X POST "https://wisdom-gate.juheapi.com/v1/videos" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F model="sora-2" \
  -F prompt="A serene lake surrounded by mountains at sunset" \
  -F seconds="15"

Step 2: Check progress

curl -X GET "https://wisdom-gate.juheapi.com/v1/videos/{task_id}" \
  -H "Authorization: Bearer YOUR_API_KEY"

Use async polling to avoid blocking the lesson assembly process. Attach the video once ready.

Quality, Evaluation, and Safety

Ensure your education AI generator meets teaching standards:

Rubric-based QA: correctness, clarity, alignment with learning objectives.
Automated checks: detect hallucinations (facts outside source), too-long labels, missing alt text.
A/B tests: diagram styles, color palettes, and label density.
Accessibility: semantic headings, ARIA roles, high contrast.
Safety: avoid generating misleading visuals; flag questionable content.

Suggested metrics:

Lesson completion rate and time-on-task.
Diagram clarity score (user ratings after exercises).
Regenerate rate (lower is better if initial outputs are clear).
Cost per published lesson.

Scaling, Caching, and Cost Control

You want reliability and predictable spend:

Cache by topic + learner_level; invalidate when the syllabus updates.
Idempotency keys to deduplicate diagram jobs.
Pre-generate popular topics off-peak to fill your CDN.
Batch diagram_briefs into a single job when possible.
Compress base64 outputs and store optimized formats (WebP/AVIF).
Circuit breakers and retries on image endpoints.

Cost modeling example:

1 lesson → 2 diagrams standard → ~0.04 USD
Optional 1 Pro diagram → +0.068 USD if needed
Optional 1 video → +0.12 USD
Estimated total (hybrid) → ~0.228 USD

Compared to official rates, this yields substantial savings while maintaining official grade quality and 10-second generation consistency.

Security and Compliance

Protect learner data and your keys:

Never hardcode API keys; use a secrets manager and short-lived tokens.
Role-based access for orchestration services.
Log prompts and outputs with PII redaction.
Watermark or label AI-generated diagrams if your policy requires it.
Provide a feedback channel for content corrections.

UX Patterns for Teaching Sites

Make the assistant feel native to your platform:

Progressive reveal: explanation, then diagram, then exercises.
Toggle difficulty (beginner/intermediate/advanced) to regenerate tailored diagrams.
“Compare variants” button to show multiple diagram styles.
Inline quiz under each diagram with immediate feedback.
Print-friendly view and downloadable assets.

Mobile-first considerations:

Use single-column diagrams; avoid tiny labels.
Provide zoom controls and pan gestures.
Prefer lightweight backgrounds; high contrast text.

Production Checklist

Observability: trace each step (explain, diagram, visualize) with timings.
SLA targets: median 10s image generation, p95 under heavy load.
Cost alerts: thresholds per course/tenant.
Backpressure: queue-based orchestration with rate limiting.
Disaster recovery: replicate prompts and outputs; keep regeneration viable.

Sample Requests and Payloads

Below is a compact sequence you can adapt.

Explain request:

POST /explain
{
  "topic": "Photosynthesis",
  "learner_level": "intermediate",
  "prior_knowledge": "cell structure"
}

Explain response excerpt:

{
  "key_points": ["Inputs: light, water, CO2", "Outputs: glucose, oxygen"],
  "diagram_briefs": [
    "Chloroplast cross-section with labels: thylakoid, stroma; arrows for light reactions",
    "Flowchart: inputs to outputs, balanced equations"
  ]
}

Nano Banana standard image request (diagram generator AI):

curl --location --request POST "https://wisdom-gate.juheapi.com/v1/chat/completions" \
  --header "Authorization: Bearer YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw '{
    "model": "gemini-2.5-flash-image",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Chloroplast diagram, clear labels: thylakoid, stroma; arrows for light-dependent reactions."}
      ]
    }],
    "stream": false
  }'

Nano Banana Pro image request:

curl --location --request POST "https://wisdom-gate.juheapi.com/v1/chat/completions" \
  --header "Authorization: Bearer YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw '{
    "model": "gemini-3-pro-image-preview",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Side-by-side: detailed chloroplast cross-section with micro-structures and balanced equation panel."}
      ]
    }],
    "stream": false
  }'

Sora AI Video (optional):

curl -X POST "https://wisdom-gate.juheapi.com/v1/videos" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F model="sora-2" \
  -F prompt="Animated flow from inputs (light, water, CO2) to outputs (glucose, oxygen) with labels" \
  -F seconds="10"

Status check:

curl -X GET "https://wisdom-gate.juheapi.com/v1/videos/{task_id}" \
  -H "Authorization: Bearer YOUR_API_KEY"

Edge Cases and Fallbacks

Prepare for the messy real world:

Ambiguous topics: ask clarifying questions or default to beginner framing.
Overcrowded diagrams: split into panels; reduce label density.
Low contrast color schemes: auto-adjust for accessibility.
Long generation times: display a skeleton UI and allow cancel/regenerate.
Inconsistent styles: use a style reference image or base64 to enforce consistency.
Localization: produce English diagrams by default; annotate with language-neutral symbols.

Next Steps

Start with a single course and measure clarity gains.
Define your prompt templates and rubric.
Integrate Nano Banana standard first; add Pro for complex topics.
Add the optional Sora video step for motion-heavy subjects.
Track costs and scale with caching and pre-generation.

Keywords to include in your metadata and on-page text: AI learning assistant, education AI generator, diagram generator AI.

Appendix: Practical Tips for Teaching Site Developers

Keep captions outcome-focused: “Trace the algorithm’s path,” “Balance the reaction.”
Generate exercises that reference the diagram directly to tie cognitive load to the visual.
Offer a one-click “explain in simpler terms” reflow that re-prompts the LLM.
Store diagram prompts and outputs to enable quick re-render when curricula change.
Maintain a small library of diagram styles per subject for brand consistency.
Monitor p95 generation time during peak class hours; scale workers accordingly.
Use CDN invalidation hooks to roll out corrected diagrams safely.
Document a content correction workflow and display change logs on lesson pages.