Lesson 2 – Human-Centered AI Strategy

Lesson Overview & Activities

1. Recap & AI Opportunity Deep Dive (15 min)

* Quick Share: Participants briefly share their chosen “AI Opportunity” and the observation about AI from their homework.

* Discussion: “What challenges or questions arose as you started thinking about implementing your AI opportunity?” This bridges the gap to human-centered design.

2. The Heart of AI: Human-Centered Design (HCD) (25 min)

* Mini-Lecture: Introduction to HCD for AI:

* Empathy: Understanding user needs, pain points, and desires.

* Define: Clearly articulating the problem to be solved (the “why”).

* Ideate: Brainstorming potential AI-powered solutions.

* Prototype: Creating low-fidelity models of the solution.

* Test: Gathering user feedback and iterating.

* Case Study: Analyzing a successful (or failed) AI product/feature through an HCD lens (e.g., a smart assistant feature, a content recommendation engine). What did they get right/wrong from a user perspective?

* Activity: “User Persona Creation”: In pairs, participants choose one “user” for their AI opportunity from Lesson 1 and create a simple persona (name, role, goals, pain points, relationship with technology).

3. Defining Value & Impact: Beyond Efficiency (20 min)

* Mini-Lecture: Differentiating between efficiency gains (e.g., saving time, reducing cost) and transformative impact (e.g., better decisions, new capabilities, improved employee/customer experience).

* Framework: “Value Proposition Canvas” for AI:

* Customer Jobs: What are users trying to get done?

* Pains: What frustrates them? * Gains: What would delight them?

* AI Pain Relievers: How does AI reduce pains? * AI Gain Creators: How does AI create gains?

* Group Activity: Applying the “Value Proposition Canvas” to their AI opportunity and user persona. “How does your AI idea directly address user pains and create tangible gains?”

4. Ethical AI & Responsible Innovation (20 min)

* Mini-Lecture: Key ethical considerations in AI:

* Bias: Data bias, algorithmic bias, how it manifests.

* Transparency & Explainability: Understanding why AI makes certain decisions.

* Privacy & Data Security: Responsible data handling.

* Fairness & Equity: Ensuring equitable outcomes for all users.

* Accountability: Who is responsible when AI makes a mistake?

* Discussion: “Identifying Potential Pitfalls”: Using their AI opportunity, participants brainstorm potential ethical issues or biases that could arise (e.g., if AI-driven recruitment, what biases might be perpetuated?).

* Action Plan Brainstorm: “How can we proactively mitigate these risks in our design?” (e.g., diverse data, human oversight, clear communication).

5. Stakeholder Engagement & Communication (10 min)

* Mini-Lecture: Importance of involving stakeholders early and often.

* Framework: Simple Stakeholder Map (identify who is affected by, involved in, and can influence the AI solution).

* Individual Activity: Participants begin sketching a stakeholder map for their AI opportunity.

🛠️ Tools & Resources

• Presentation Slides: Key concepts, examples, and discussion prompts.

• “User Persona Template” Worksheet.

• “AI Value Proposition Canvas” Worksheet.

• “Ethical AI Checklist” Handout: Simple questions to consider.

• “Simple Stakeholder Map” Template.

• Optional Reading: Short article on “Designing for AI Trust.”