| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - text-classification |
| tags: |
| - media-integrity |
| - education |
| - student-curated |
| - fake-news |
| - artificial-intelligence |
| - ai-literacy |
| - misinformation |
| - deepfake-detection |
| pretty_name: AIYA Events Toolkits & Benchmark Data |
| size_categories: |
| - n<1K |
| --- |
| |
| # AIYA Events Toolkits & Benchmark Data 🛠️ |
|
|
| This dataset repository provides student-curated evaluation benchmarks and structured data assets supporting the **AI Youth Alliance (AIYA)** global events program. |
|
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| The resources in this repository are associated with three AIYA tentpole events: |
|
|
| - **AIYA Literacy Week** — hands-on challenges and exercises focused on foundational AI literacy and practical AI fluency. |
| - **AIYA Global AI Hackathon** — an international student competition focused on building applications, models, agents, and solutions using AI. |
| - **AIYA Impact Symposium** — a global showcase for student projects applying artificial intelligence to real-world problems and endeavors. |
|
|
| For current event information, dates, participation details, and announcements, visit: |
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| **https://www.aiyouthalliance.org/events** |
|
|
| > **Event Information:** The official AIYA Events page is the authoritative source for current event information. This repository contains supporting datasets, benchmarks, and event resources. |
|
|
| --- |
|
|
| ## 📁 Repository Structure |
|
|
| *UNDER CONSTRUCTION* |
|
|
| - `sample.csv` — Foundational evaluation rows for student media literacy and AI evaluation workflows. |
| - `/literacy-week-toolkit/` — Data-driven challenges and exercises highlighting the capabilities and limitations of AI. |
| - `/hackathon-toolkit/` — Benchmarking datasets and supporting resources for AIYA Global AI Hackathon challenges. |
| - `/impact-symposium-toolkit/` — Reference datasets and validation resources supporting AIYA Impact Symposium projects and submissions. |
|
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| --- |
|
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| ## ⚠️ Academic Notice & Cognitive Well-Being |
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| AIYA encourages students to use generative AI and other AI systems critically and deliberately. |
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| AI should be used as a **force multiplier for human thinking**, not as a replacement for independent analysis, subject understanding, creativity, or genuine authorship. |
|
|
| Students are encouraged to: |
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|
| - Question AI-generated outputs |
| - Verify information independently |
| - Test model capabilities and limitations |
| - Maintain analytical and problem-solving skills |
| - Preserve agency and authorship in their own work |
|
|
| --- |
|
|
| ## 🚀 Getting Started with the Data |
|
|
| ### 1. Previewing Data Online |
|
|
| You can preview, filter, and download the data using the interactive **Dataset Viewer** on this Hugging Face page. |
|
|
| ### 2. Loading via the Datasets Library |
|
|
| To load the dataset into Python or a Jupyter Notebook: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("aiyouthalliance/aiya-media-integrity-benchmarks") |
| |
| print(dataset["train"][0]) |
