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Source-provided image accompanying New NSF-backed project aims to help middle schoolers decide what to trust in the age of AI
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The University of Denver and The Tech Interactive have received a $50,000 planning award through the NSF FINDERS FOUNDRY program for a project that aims to help middle schoolers build a skill they increasingly need: the ability to determine what information to trust.

The University of Denver and The Tech Interactive have received a $50,000 planning award through the NSF FINDERS FOUNDRY program for a project that aims to help middle schoolers build a skill they increasingly need: the ability to determine what information to trust.

The team will co-design a tool to help students independently and critically evaluate AI-generated information.

The project builds on an existing collaboration between Dr. Pilyoung Kim and The Tech, where she served as a key advisor, developing AI literacy lessons for the organization’s National AI Literacy Day activations.

The project aims to create a tool that reflects how students actually encounter AI-generated information at school, at home, and in daily life.

The goal is to help students build agency, not fear, as they grow up in an AI-driven world.

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The project aims to create a tool that reflects how students actually encounter AI-generated information at school, at home, and in daily life. The goal is to help students build agency, not fear, as they grow up in an AI-driven world.

The project aims to create a tool that reflects how students actually encounter AI-generated information at school, at home, and in daily life.

The goal is to help students build agency, not fear, as they grow up in an AI-driven world.

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Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
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The project will co-design a tool to help students independently and critically evaluate AI-generated information.

The project will co-design a tool to help students independently and critically evaluate AI-generated information.

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