DCG AI Course Design Co-Creation Tool (Online Preview)
Teachers remain the experts. We provide the thinking scaffold; AI accelerates and structures your expertise.
In one sentence
An online co-pilot that guides structured thinking and produces a full outline in one click.
Define your teaching “atom”
A single minimalist card asks three essential questions to anchor the lesson context.
👉 Your task: answer the three prompts and the system captures the full context.
| Field | Input | Purpose |
|---|---|---|
| ① Core Concept | AI Bias | Set the knowledge focus for the lesson. |
| ② Learner Group | Grade 8 (dropdown) | Align the design with learner age. |
| ③ Cultural Context | Mainland China (dropdown) | Align the design with the local culture. |
Co-create with AI using four guiding questions
After you start building, four cards gather insights from analogy, context, prerequisites, and classification—each with prompts, examples, and instant AI support.
Analogy
Which familiar experience can you compare this concept to? A precise analogy lights up the lesson.
To help students grasp “AI bias”, I would compare it to...
[A robot wearing tinted glasses, or a picky eater]
Example: To explain “neural networks”, say it works like the human brain.
Context
What local or lived example will resonate most with your learners?
For “AI bias”, the most relatable scenario for my students is...
[Beauty apps that generate identical influencer faces]
Example: When teaching algorithms, use the short-video recommendation logic they see every day.
Prerequisite
What foundational knowledge must students grasp first?
Before students understand why “AI bias” happens, they must know...
[AI learns patterns from large datasets]
Example: Before equations, learners must understand variables.
Classification
Which broader domain does this concept belong to? Build a mental map for students.
The issue of “AI bias” actually belongs to...
[AI ethics and social responsibility]
Example: “Photosynthesis” sits inside “Life Science”.
When all four cards light up
AI combines your insights and unlocks the “✨ Generate outline” button.
Sample: Intelligent Lesson Outline
Below is an example micro-lesson outline generated by the system. Each block cites the source card so you can retrace design decisions.
Lesson Title: Does AI have “little biases”? — Exploring beauty apps and AI bias
Learners: Grade 8 · Context: Mainland China
Core objectives
- Explain AI bias in their own words.
- Analyse root causes of AI bias through everyday examples.
- Develop an initial awareness of AI ethics and responsibility.
From the Analogy card
Part 1 · Lesson hook (analogy warm-up)
Activity: Ask “Can a robot be picky or wear tinted glasses?” to spark curiosity.
Explain: Sometimes AI behaves like a picky eater—that behaviour is what we call bias.
From the Context card
Part 2 · Core instruction (case analysis)
Case: Show beauty-filter app outputs and ask why they all look the same.
Highlight: This uniform aesthetic reveals algorithmic bias.
From the Prerequisite card
Part 3 · Concept deep dive (knowledge ladder)
Explain: AI learns only from the data we feed it; if we only share influencer photos, it assumes that is universal beauty.
Conclusion: Biased data leads directly to biased AI.
From the Classification card
Part 4 · Extend and reflect (broader lens)
Discussion: AI bias is more than a technical bug—it is part of AI ethics and responsibility. How can future systems stay fair?
Suggested class activity
Have teams investigate whether game recommendations, news feeds, or translators also show AI bias, and share examples.
Want to export the outline as a lesson plan? PDF and DOCX export is coming soon.
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