Module 1: The 80% Problem – Why AI Systems Fail

Diagnose the fundamental design flaws causing widespread AI failure

• The Forecasting Fallacy: Why data-driven prediction creates humanly irrelevant systems
• The three missing human elements: Intent, Foresight, and Agency
• Case study analysis: Decoding high-profile AI failures
• “Putting AI on a pig”: How companies automate dysfunction instead of solving problems
• Exercise: Identify which of the three gaps (Intent, Foresight, Agency) are present in 2-3 AI products you use daily. (Goal: Just name the gap, not solve it yet.)

Module 2: From Design Thinking to Anticipatory Design

Understand why traditional UX methods fall short for AI systems

• The limitations of present-focused design for future-predicting systems
• Why AI requires a different mindset: Predicting and shaping future behavior
• Introduction to Foresight: Moving from “what is” to “what could be”
• Backcasting vs. Forecasting: Working backward from desired futures
• Exercise: Map your current design process and identify where it’s present-focused vs. future-oriented. And identify opportunities where you could add a future-oriented focus.

Module 3: Trade-offs and Ethical Considerations

Understand the technical landscape and inherent trade-offs

• Precision vs. Explainability: When accuracy sacrifices transparency
• Personalization vs. Privacy: Balancing adaptation with data protection
• Automation vs. Agency: How much control should users have?
• Proactive vs. Reactive: When should AI intervene vs. wait?
• Exercise: Pick one AI product you use. Identify at least 2 trade-offs the designers made (e.g., “Spotify automates playlists [Automation] but limits manual control [Agency]”). Discuss: Would you design it differently?

Module 4: Behavioral Foundations (Part 1: Agency)

Understand why traditional UX methods fall short for AI systems

• The limitations of present-focused design for future-predicting systems
• Why AI requires a different mindset: Predicting and shaping future behavior
• Introduction to Foresight: Moving from “what is” to “what could be”
• Backcasting vs. Forecasting: Working backward from desired futures
• Exercise: Map your current design process and identify where it’s present-focused vs. future-oriented. And identify opportunities where you could add a future-oriented focus.

Module 5: Behavioral Foundations (Part 2: Personalization)

Apply Prochaska’s Transtheoretical Model for stage-adaptive AI

• The five stages of change: Precontemplation → Maintenance
• Why one-size-fits-all AI fails: The personalization imperative
• Designing stage-appropriate interventions for beginners vs. experts
• Avoiding premature optimization: Matching AI behavior to user readiness
• Exercise: Map user journey stages and design differentiated AI responses

Module 6: Behavioral Foundations (Part 3: Context)

Master contextual timing with Nudge Theory

• Context as catalyst: Why the right intervention at the wrong time fails
• Nudge Theory fundamentals: Choice architecture for AI
• Cognitive, emotional, and situational context: When to act vs. when to recede
• Ethical boundaries: Designing AI that respects intent, not just predicts patterns
• Exercise: Create contextual intervention rules for an AI system

Module 7: Anticipate – Understanding Emerging Behaviors

Learn to identify trends and weak signals that shape user futures

• Introducing the AIS Framework: How Anticipate → Imagine → Shape integrates the behavioral tools you’ve learned (Fogg, Prochaska, Nudge)
• Project kickoff: Select one AI product/feature to redesign using the complete AIS Framework (this will be your through-line for Modules 7-9)
• The Anticipate phase: Moving from current user research to emerging behaviors
• Beyond user research: How to integrate trend analysis with qualitative methods
• Application of the STEEP tool: Analyzing Social, Technological, Economic, Environmental, and Political forces
• Weak signal detection: Spotting emerging behaviors before they become obvious
• Workshop: Conduct a trend analysis for the selected AI product
• Deliverable: Trend map identifying forces that will change user behavior

Module 8: Imagine – Designing for Multiple Futures

Master scenario planning and backcasting for adaptive AI systems

• Scenario planning fundamentals: Creating optimistic, pessimistic, and alternative futures
• The 4-step backcasting process: From desired future to today’s decisions
• Avoiding single-future traps: Why “the best solution” thinking fails in AI
• How to prepare cross-functional scenario workshops: Aligning designers, PMs, and engineers
• Workshop: Create 3 scenarios for YOUR AI product (from Module 7) and backcast from each
• Deliverable: Scenario plans with a backcasting roadmap for your project

Module 9: Shape – Prototyping and Iteration

Transform scenarios into testable, behavioral systems

• From scenarios to prototypes: Making futures tangible
• Iterative design for AI: Build → Test → Learn → Refine
• Calibrating the three frameworks: Integrating Fogg, Prochaska, and Nudge in practice
• Behavioral alignment testing: Validating beyond usability (methods + metrics)
• Workshop: Prototype and test the scenario(s) (from Module 8) using all three behavioral frameworks

Module 10:  Synthesis and Presentation

Package and present the complete case study

• Synthesizing the work: Integrating Anticipate → Imagine → Shape into one coherent narrative
• Packaging the case study: Presenting the work professionally (structure, storytelling, visual communication)
• Creating a validation plan: Demonstrating how students would test behavioral alignment in the real world
• Peer review and feedback session