Virtual SessionWelcome & Framing (5 mins)
Interactive discussion: “What do my algorithms say about me?” (5mins):
- Share quick examples of algorithmic predictions (e.g., TikTok or Spotify feeds).
- Participants reflect: “What would your students’ algorithms say about them?”
Discussion: “AI isn’t truth—it’s prediction.”
Mini-Lesson: How AI Works (and Why Bias Matters) (10 mins)
- Quick overview: AI → ML → Generative AI.
- Case study: Amazon’s biased hiring algorithm to illustrate bias in data sets.
- Prompt: “What classroom conversations could this spark?”
Case Study Exploration (15 mins)
Participants work through condensed versions of two student scenarios:
Scenario 1: Using ChatGPT for an essay in a class without an AI policy.
Scenario 2: AI recommendation algorithms on social media.
Each small group discusses tradeoffs and drafts a “conscious AI decision statement.”
Build Your Personal AI Code (10 mins)
Facilitator models creating their own code.
Participants complete a short reflective framework:
Identify core values (e.g., integrity, growth, privacy).
Draft 2-3 personal statements that guide conscious AI use.
Classroom Debrief (5 mins)
Discuss: “How could this process look in your classroom?”
Identify curriculum integration points (e.g., digital citizenship, English reflection journals, computer science ethics units).
Commitment + Closing Reflection (5 mins)
Prompt: “One conscious AI choice I’ll commit to modeling this year is…”
Takeaway: Link to free resource folder + editable Personal AI Code template.
-Help students name what integrity looks like in an AI-integrated world by examining real classroom and personal scenarios.
-Guide students through creating their own Personal AI Code so they can make conscious, values-aligned choices about how and when to use AI.
-Model reflective conversations that help students recognize the difference between AI that enhances learning and AI that replaces it.
-Use curiosity-driven discussion and reflection tools to explore ethical dilemmas around privacy, bias, and academic pressure.
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way.
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.
https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about
https://www.aclu.org/news/womens-rights/why-amazons-automated-hiring-tool-discriminated-against
Back
Trips and Tours