Week 7: Artificial Intelligence, Continued

Beyond supervised learning — how machines optimize, discover, and generate.

Week 7 Schedule and Activities

In Week 7, you will:

The schedule below is a suggested pacing guide. Feel free to adjust based on your own calendar, but try to keep the order of activities so later pieces build on earlier ones.

Week 7 is not assessed on a Competency Demo. The topics this week are important for your development as a CS educator, but the formal assessment for the AI unit was CD #6 at the end of Week 6. Engage with the material here because it matters — not because it will be tested.

Computer Science Content
(Monday – Wednesday)

  • Topic 7a – Reinforcement Learning: Hill Climbing and Genetic Algorithms
    Go deep on reinforcement learning through two concrete techniques. Work through hill climbing and genetic algorithms — each with a different strategy for searching a solution space and improving over time — and understand why one gets stuck where the other keeps going.
  • Topic 7b – Unsupervised Learning and Pattern Discovery
    Study the third major category of machine learning: systems that find structure in data without any labeled examples or predefined categories. Understand clustering and where unsupervised learning already operates quietly in recommendation systems, medical imaging, and data mining.
  • Topic 7c – Large Language Models
    Understand what ChatGPT, Claude, and similar tools actually are: how they are trained, what they are doing when they generate text, why they sometimes produce confident nonsense, and what that means for a teacher deciding when and how to use them. This topic is intentionally placed last — everything before it is context for understanding it clearly.

Beyond the Content
(Thursday – Friday)

The Social, Ethical, and Teaching perspectives for the Week 7 content are here, covering reinforcement learning, unsupervised learning, and large language models. You now have the complete two-week picture, which makes for a richer and more honest conversation.

  • Social and Ethical Considerations
    Examine scenarios connecting AI — from reward misalignment in recommendation systems to hallucination in AI grading tools to the broader question of what automation means for the future of work and what we teach students to prepare for it.
  • Teaching and Learning Perspectives
    Reflect on how reinforcement learning, unsupervised learning, and LLMs translate to your classroom. What misconceptions do students at each grade level bring? And what does it mean to teach students to use AI tools wisely rather than uncritically?

Small Group Discussion
(Thursday – Friday)

You will meet with your small group to make sense of both AI weeks together. This is a synthesis conversation — what patterns do you see across the full AI arc, what surprised you, and what feels most relevant to your teaching context?

Week 7 Small Group Discussion Instructions

AI Post-Reflection
(Thursday – Saturday)

At the start of Week 6, you wrote down what you believed about artificial intelligence before the readings shaped your thinking. It is time to go back and read what you wrote.

The AI Post-Reflection asks you to revisit those responses and reflect on how two weeks of study have shifted — or complicated, or confirmed — your thinking. This is the second half of an activity that started on the first day of Week 6.

Complete the AI Post-Reflection →

Submit your responses on Blackboard by end of day Saturday, August 2.

CD #6 retakes, if needed, are also due by August 6.