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  license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: mit
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+ task_categories:
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+ - text-classification
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+ tags:
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+ - media-integrity
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+ - education
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+ - student-curated
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+ - fake-news
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+ pretty_name: AIYA Media Integrity Benchmarks & Event Toolkits
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+ size_categories:
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+ - n<1K
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: sample.csv
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  ---
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+
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+ # AIYA Events Toolkits & Benchmarks 🛠️
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+
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+ This dataset repository serves as the official, foundational entry point for local school chapters of the **AI Youth Alliance (AIYA)**. It houses student-curated evaluation benchmarks alongside the structured data assets used for **AIYA Literacy Week**, the **AIYA Global Hackathon**, and the **AIYA Impacgt Symposium**.
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+
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+ ## 📁 Repository Structure - UNDER CONSTRUCTION
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+ * `sample.csv` — Foundational evaluation rows for student media literacy tracking.
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+ * `/literacy-week-toolkit/` — Data-driven challenges and exercises highlighting the pathologies and potential of AI.
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+ * `/hackathon-toolkit/` — Standardized benchmarking datasets for modeling and building challenges.
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+ * `/impact-symposium-toolkit/` — Reference datasets and validation sets used for project submissions.
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+
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+ ## ⚠️ Academic Notice & Cognitive Well-Being
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+ In alignment with recent cognitive science research (including landmark studies from MIT), AIYA explicitly warns against over-reliance on generative tooling. Offloading critical thought to automated systems causes measurable atrophy in analytical capacity, genuine subject mastery, and real authorship.
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+ Use the benchmark datasets in this repository to explore AI as a **force multiplier for your mind**, not a replacement for your intellect.
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+ ## 🚀 Getting Started with the Data
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+ ### 1. Using the Hugging Face Web Interface
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+ You can preview, filter, and download the data directly using the interactive **Dataset Viewer** tab at the top of this page.
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+
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+ ### 2. Loading via the Datasets Library
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+ To pull this data directly into your Python scripts or Jupyter Notebooks, install the `datasets` library and run:
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load the student-curated benchmark dataset
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+ dataset = load_dataset("aiyouthalliance/aiya-media-integrity-benchmarks")
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+ print(dataset['train'][0])
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+ ```
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+
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+ ## 🤝 Local Chapter Coordination
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+ Coordinate with your school's **Faculty and Administrative Advisor** to clear local school network guidelines regarding dataset access. For support, reach out to your local chapter lead or contact us directly at **[aiyouthalliance.org](https://aiyouthalliance.org)**.