---
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.

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:

**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.

---

## ⚠️ Academic Notice & Cognitive Well-Being

AIYA encourages students to use generative AI and other AI systems critically and deliberately.

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:

- 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])