PK
Concepts were quiet easy to understand and it really helped with my AI engagement, especially with my career planning.

This course teaches students how to collaborate with AI effectively, efficiently, ethically, and safely across academic and career contexts. Through the 4D Framework (Delegation, Description, Discernment, and Diligence), students develop lasting skills that go far beyond prompt tricks, learning to use AI as a thinking partner that enhances rather than replaces their own critical thinking and creativity. Participants will learn how to leverage AI to understand concepts more deeply, strengthen professional skills, and prepare for a future where AI fluency is essential. The course emphasizes being the human in the loop, maintaining agency, judgment, and responsibility while working thoughtfully with AI systems. Built through a long-standing partnership between Anthropic and professors Rick Dakan (Ringling College of Art and Design) and Joseph Feller (University College Cork), this course addresses a fundamental question students face today: how can I use AI to genuinely enhance my learning and career success without compromising my own growth and integrity? Recommended prerequisites: Completion of AI Fluency: Framework & Foundations is recommended for deeper understanding. Learners should have access to an AI chat tool for hands-on practice. Examples use Claude.ai, but any chatbot will work.

PK
Concepts were quiet easy to understand and it really helped with my AI engagement, especially with my career planning.
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True AI fluency means shifting from a "consumption" mindset to a "collaboration" mindset. Students often mistake AI for a machine that gives instant, final answers. In reality, fluent students treat AI as a critical thinking partner and an elite sounding board. The ACT Framework (Augment, Create, Trust/Verify): Fluent students understand that AI excels at augmenting human capability. It handles the rote, repetitive sub-tasks so humans can focus on high-level strategy, creative problem-solving, and architecture. Human-in-the-Loop (HITL): No matter how advanced large language models (LLMs) become, the student remains the final decision-maker. This means reviewing, questioning, and stress-testing every snippet of code, mathematical derivation, or prose generated by an AI before it is ever put into practice.
Concepts were quiet easy to understand and it really helped with my AI engagement, especially with my career planning.
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