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Tuesday May 12, 2026 9:30am - 10:30am PDT
AI systems often produce responses that sound fluent, confident, and complete—yet something feels off. In this hands-on lab, participants will actively generate, interrogate, and revise AI outputs to learn how to distinguish real rigor from confident nonsense.

Working in a learner-driven model, participants will engage in live, iterative AI collaboration: posing questions, revising inputs, and tracking how meaning shifts under changing constraints. Mathematics is used as a language for interrogation—revealing structure, crystallizing patterns, naming variables, and surfacing assumptions early. Rather than evaluating outcomes after harm accumulates, participants practice applying pressure at the start of the feedback loop.

This process frames AI collaboration as an ethical practice: justification, revision, and accountability from within a system. Equity emerges as a structural outcome of this practice—visible when missing perspectives are named, assumptions are challenged, and designs are regularly revised to change what becomes possible.

Participants experience a repeatable, classroom-ready structure that positions students as distributed authors and sense-makers, not passive recipients of AI output. The session closes by connecting this practice to life beyond school—preparing learners to orient, decide, and act with agency inside complex systems where outcomes are uncertain and stakes are real.

Speakers
avatar for Tia Knuth

Tia Knuth

Founder, JoyMath
Tia Knuth, founder of JoyMath, has a deep relationship with mathematics, writing, and forest ecosystems. Her work explores how patterns, structure, and human agency intertwine, and how shared practices can change what becomes possible.
Tuesday May 12, 2026 9:30am - 10:30am PDT
Shiley Hall 312 Shiley Hall, 5000 N Willamette Blvd, Portland, OR 97203, USA

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