Foundations: Understanding UbD + AI
Welcome to your journey! This module, you'll explore the backward design philosophy and discover how AI can serve as your intelligent co-designer.
Learning Outcomes
By the end of this module, you will be able to:
- ✓ Explain the three stages of Understanding by Design (UbD) and why "backward design" works
- ✓ Describe how AI can serve as a co-designer while you maintain control
- ✓ Explain how AI tools generate responses and why their outputs require expert review
- ✓ Write your first AI prompt for course design
- ✓ Identify a course you want to design or redesign during this program
Dexi Says:
Don't worry if you've never used AI before! I'll guide you through everything step by step. All you need is curiosity and a course idea.
What is Understanding by Design?
Understanding by Design (UbD) ↗ is a framework for designing courses created by Jay McTighe and Grant Wiggins. It's called "backward design" because you start with the end in mind.
Instead of asking "What should I teach?" you ask "What should students be able to understand and do by the end?"
Why UbD for AI-Assisted Course Design?
UbD is especially powerful when working with AI tools. Because backward design requires you to clarify your goals, evidence, and learning plan before building content, it gives you a clear framework for directing AI—and for evaluating whether AI-generated outputs actually serve your students' learning. Without clear goals, AI produces generic content. With UbD as your guide, every AI prompt has a purpose, and every AI output can be measured against your intended results.
Why "Backward"?
Traditional design goes: Content → Activities → Test. UbD flips this: Goals → Evidence → Activities. You design assessments BEFORE planning lessons!
The Three Stages of UbD
Desired Results
What should students understand, know, and be able to do?
- Transfer goals
- Essential questions
- Learning outcomes
Evidence
How will we know if students have achieved the goals?
- Performance tasks
- Rubrics & criteria
- Supporting assessments
Learning Plan
What activities will help students learn and demonstrate understanding?
- Learning activities
- Lesson structures
- Course schedule
Backward design in action: Each stage builds on the previous one. You define what matters (Stage 1), determine how you'll measure it (Stage 2), and then plan how to get students there (Stage 3).
🎬 Understanding by Design Video
Watch this overview of the Understanding by Design framework to deepen your understanding of backward design principles.
Video not playing? Click here to watch on YouTube
📄 Prefer reading? View the text summary of this video
Understanding by Design (UbD) is a curriculum design framework developed by Jay McTighe and Grant Wiggins. The core idea is "backward design"—instead of starting with textbooks or activities, you start by identifying what students should understand and be able to do, then work backward to plan assessments and learning experiences.
The framework has three stages. Stage 1 (Desired Results) asks you to define transfer goals, essential questions, and learning outcomes—the "big picture" of what matters most. Stage 2 (Evidence) asks you to determine how you will know if students have achieved those results, through performance tasks, rubrics, and other assessments. Stage 3 (Learning Plan) is where you design the activities, lessons, and instruction that help students develop the knowledge and skills needed to demonstrate understanding.
The key insight is that assessments are designed before lesson plans. This ensures that your teaching is purposefully aligned with what you're measuring, and that every activity serves a clear learning goal. UbD helps educators avoid "coverage-based" teaching—where content is taught simply because it appears in a textbook—and instead focus on deep understanding and real-world transfer.
📝 Quick Check: In UbD, when do you design your assessments?
How AI Tools Actually Work
Before you start using AI for course design, it helps to understand what AI actually does when you send it a prompt. This isn't about technical details—it's about building a practical mental model that explains why every activity in this program asks you to think first and critically evaluate AI output afterward.
What Happens When You Send a Prompt
You Send a Prompt
You type a question or instruction in natural language. The more context you provide, the better the output.
AI Predicts Patterns
The AI analyzes your text and generates a response word by word, predicting the most likely next word based on patterns in its training data.
You Evaluate the Output
The response may be helpful, incomplete, or wrong. Your expertise determines what to keep, revise, or reject.
The Key Insight
AI doesn't understand your course, your students, or your discipline. It produces statistically likely text based on patterns. This is why it can sound confident and polished while being factually wrong—and why your expert judgment is essential at every step.
What You Need to Know
Pattern Prediction, Not Understanding
AI generates text by predicting the most probable next word in a sequence, based on patterns learned from vast amounts of text. It doesn't "know" things the way you do—it recognizes and reproduces patterns. This is why it can write fluently about topics without truly comprehending them.
Hallucination Risks
AI can generate information that sounds authoritative but is entirely fabricated—a phenomenon called "hallucination." It may invent citations, create plausible-sounding statistics, or confidently state incorrect facts. This is not a bug that will be fixed; it's inherent to how pattern prediction works.
Training Data Limitations
AI models are trained on large datasets of text from the internet, books, and other sources. This means they can reflect biases present in that data, may lack current information, and may not represent all disciplines, cultures, or perspectives equally. The AI doesn't know what it doesn't know.
Why Your Expertise Matters
You bring something AI cannot: deep knowledge of your discipline, understanding of your specific students, awareness of your institutional context, and professional judgment about what constitutes quality in your field. AI can draft and brainstorm; only you can decide what's right for your course.
This Is Why We Use the Human–AI–Human Pattern
Every activity in AI DesignLab asks you to think first (because AI can't replace your expertise), use AI to expand and refine (because it's excellent at brainstorming and drafting), and then critically evaluate (because AI output may contain errors, biases, or generic content that doesn't fit your context). Understanding how AI works is the foundation for using it well.
Dexi Says:
Think of me like a very fast research assistant who has read a lot but doesn't truly understand any of it. I can help you brainstorm, draft, and organize—but I need you to check my work and make the important decisions!
AI as Your Co-Designer
AI tools like ChatGPT, Claude, and Gemini can dramatically speed up your course design process. But here's the key insight:
AI is a powerful assistant, not a replacement for your expertise.
Think of AI as a tireless collaborator who can brainstorm ideas, draft content, suggest alternatives, and handle routine tasks—while you provide the vision, judgment, and quality control. In AI DesignLab, every activity follows a Human–AI–Human pattern: you reflect and draft first, AI helps refine and expand, and then you critically evaluate and make the final decisions.
Our Design Philosophy
AI as Co-Designer
AI assists and suggests; you decide and refine. It generates options, you make choices.
Human Control
Every AI output requires your review. You approve, modify, or reject suggestions.
Ethics & Equity
Always check AI outputs for bias, accessibility, and appropriateness for your learners.
Critical Review
AI can make mistakes. Verify facts, check for hallucinations, and trust your expertise.
Important Reminder
AI outputs are starting points, not final products. Your professional judgment, knowledge of your students, and understanding of your context are irreplaceable.
What AI Can Help With
- ✓ Brainstorming learning outcomes and assessment ideas
- ✓ Drafting assessment tasks and rubrics
- ✓ Generating activity ideas and lesson plans
- ✓ Creating variations for different learning levels
- ✓ Checking alignment between goals, assessments, and activities
Your First AI Prompt
Let's put theory into practice! Every activity in AI DesignLab follows the DesignLab Method—a four-step pattern that keeps you in control: Reflect, Rough Draft, AI Refine, and Critically Evaluate.
Choose Your Course Project
-
Think about your course
Choose a course you want to design (or redesign) during this program. It could be a new course, an existing course you want to improve, or a workshop/training program.
-
Note the basics
What's the subject? Who are your learners? What level (intro, intermediate, advanced)? How long is the course?
-
Open an AI tool
Go to any free AI chatbot and create a free account if needed:
ChatGPT ↗ — Free tier available; sign up with email or Google account
Claude ↗ — Free tier available; sign up with email or Google account
Gemini ↗ — Free with any Google account
Microsoft Copilot ↗ — Free with any Microsoft account
Your First Prompt
Return to DesignLab After Each AI Interaction
When you copy a prompt and paste it into your AI tool, remember to come back here afterward! Copy the AI's response, return to this page, and continue with the next step. AI chatbots can easily lead you down rabbit holes—DesignLab keeps your design process structured and focused.
Step 1: Reflect
Before writing anything, take 5 minutes to think about these questions:
1. What are 2–3 "big ideas" or enduring understandings you want students to walk away with?
2. What essential questions could drive your course?
3. What makes this course meaningful to you and your students?
Step 2: Rough Draft
Now jot down your own answers to the questions above. They don't need to be polished—even rough notes will give you a strong baseline for evaluating what the AI generates.
Now copy and customize this prompt to introduce yourself and your course to the AI:
I am an educator designing a course and would like your help as a co-designer. Course Subject: [Enter your subject, e.g., "Introduction to Psychology"] Target Learners: [Who are your students? e.g., "First-year university students"] Course Length: [e.g., "One semester, 15 weeks"] Current State: [New course / Redesigning existing course] I'll be using Understanding by Design (UbD) to develop this course, starting with desired results, then assessment evidence, and finally learning activities. To start, please give me: 1. A draft course description 2. 3 possible "big ideas" or enduring understandings that could be central to this course
Step 4: Critically Evaluate
Now compare the AI's suggestions with your own rough draft. Ask yourself:
• Did the AI capture the essence of what matters in your course?
• Which suggestions resonate with your expertise and which miss the mark?
• What would you keep, revise, or reject entirely?
Your professional judgment is what transforms AI output into a meaningful course design.
Save your refined big ideas and essential questions! You'll use them as starting inputs in Module 2, where they will inform your transfer goals and be developed into polished essential questions.
Pro Tip: The Power of Context
The more context you give AI, the better its suggestions. Include your teaching style, constraints, student backgrounds, or institutional requirements. Don't be afraid to have a conversation!
Dexi Says:
Remember, AI responses are starting points! Look at what it generates, keep what resonates, modify what needs work, and ask follow-up questions. You're in charge.
How This Feeds Into Module 2
The big ideas and essential questions you just explored aren't a one-time exercise—they're the raw material for your course design. In Module 2, you'll use them as starting inputs:
• Your big ideas will help you articulate transfer goals—what students should be able to do independently, long after the course ends.
• Your essential questions will be refined into polished, course-driving questions that connect directly to those transfer goals.
Save your work from this activity so you have it ready to build on next module.
Meet Your Fellow Designers
Throughout this program, you'll follow two fictitious instructors as they work through the same design process you are. Their examples show how real educators in different disciplines make decisions at each stage—what they chose, what they rejected, and why. Dexi remains your AI guide; these instructors model the human side of the design process.
Dr. Sarah Chen — Nursing
Course: Foundations of Patient-Centered Care
Context: 2nd-year BSc Nursing, 13-week semester, 60 students, clinical placements alongside lectures
Situation: Sarah is redesigning an existing course that has relied heavily on lecture-and-exam formats. She wants to shift toward authentic assessments that mirror real clinical decision-making while meeting accreditation requirements.
Her starting big ideas: Patient-centered care requires integrating clinical knowledge with empathy and communication. Safe practice depends on recognizing when you don't know something and seeking help.
Prof. Marcus Rivera — Sociology
Course: Social Inequality and Justice
Context: 3rd-year undergraduate, 13-week semester, 35 students, seminar/discussion format with a community engagement component
Situation: Marcus is building a brand-new course. He wants students to move beyond describing inequality to analyzing its root causes and advocating for change—but he's cautious about AI generating superficial analyses of complex social issues.
His starting big ideas: Inequality is structural, not just individual. Understanding injustice requires examining whose perspectives are centered and whose are marginalized.
Dexi Says:
You'll see Sarah and Marcus at every stage of this program, showing how they used AI—and when they overruled it. Their examples aren't templates to copy; they're thinking partners to learn from. Your course will look different, and that's the point!
Module 1 Reflection
Take a moment to reflect on what you've learned and experienced this module.
Your Reflection
Save Your Work
Your reflections will be saved to your browser's local storage. For a permanent copy, download the Course Design Workbook and record your progress there.
📖 Further Reading for This Module
Want to go deeper into the frameworks and concepts introduced this module? These scholarly sources provide the foundation for the ideas in this module.
Wiggins, G. P., & McTighe, J. (2005). Understanding by design. Ascd.
🎉 Module 1 Complete!
Great work! You've learned the foundations of UbD and started your AI co-design journey.
Coming up in Module 2: We'll dive deep into Stage 1 of UbD—defining your desired results, including transfer goals, essential questions, and learning outcomes. Bring your big ideas and essential questions from this module's prompt—they'll be your starting inputs.