King-8/help-request-messages-v2

---
task_categories:
- text-classification
---
# 📊 Help Classifier Dataset (v2)

## 🧠 Overview

The **Help Classifier Dataset (v2)** is a curated NLP dataset designed to classify student help requests into meaningful categories within a collaborative learning environment.

This dataset was developed as part of a larger AI system for the **Coding in Color (CIC)** ecosystem, where students work across domains such as AI development, game development, 2D/3D art, and robotics.

The goal of this dataset is to enable models to:

* Understand real student communication
* Classify intent behind help requests
* Support downstream systems (e.g., generators, agents, MCP tools)

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## 🚀 Version Update (v1 → v2)

### 🔹 v1

* ~100 examples
* Basic structure
* Limited variation
* Primarily clean, structured inputs

### 🔹 v2 (Current)

* **1,000 examples**
* Balanced across all categories
* High variation in tone and structure
* Includes:

* informal/slang language
* mixed capitalization
* short + long messages
* indirect and ambiguous requests
* real CIC-inspired check-in data

👉 v2 significantly improves **generalization and realism**

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## 🧩 Task Definition

**Task Type:** Text Classification

**Input:** Student message (free-form text)
**Output:** One of 5 help categories

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## 🏷️ Labels

| Label | Description |
| ------------------ | --------------------------------------------------- |
| `learning_help` | User is trying to understand a concept or skill |
| `project_help` | User needs direction or next steps in a project |
| `technical_issue` | Something is broken or not working as expected |
| `attendance_issue` | User missed a meeting or needs to catch up |
| `general_guidance` | User expresses uncertainty, stress, or needs advice |

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## 📦 Dataset Structure

Each example contains:

```json
{
"text": "I missed the meeting and now idk what we’re doing",
"label": "attendance_issue"
}
```

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## 📊 Dataset Statistics

* **Total Examples:** 1,000
* **Classes:** 5
* **Distribution:** Balanced (~200 per class)

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## 🎯 Design Philosophy

This dataset was intentionally designed to reflect **real-world student communication**, including:

* Natural language (not overly cleaned)
* Mixed tone (formal + casual)
* Realistic ambiguity
* Multi-intent phrasing (but single-label classification)

The dataset progresses in complexity across batches:

1. Clean structured examples
2. CIC-specific scenarios
3. Messy/realistic inputs
4. Edge cases and ambiguity
5. Advanced multi-layered messages

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## 🧪 Use Cases

This dataset can be used for:

* Help request classification systems
* Educational AI assistants
* Slack/Discord message classification
* MCP (Model Context Protocol) pipelines
* Routing systems for AI agents

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## 🔗 System Context (CIC Ecosystem)

This dataset is part of a broader system that includes (or will include):

* Help Classifier (this dataset)
* Help Generator (response generation)
* Help Summarizer (context summarization)
* MCP Server integration

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## ⚠️ Limitations

* Single-label classification (some inputs may contain multiple intents)
* Domain-specific (focused on student tech environments)
* Informal language may introduce edge ambiguity

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## 🔮 Future Improvements

* Multi-label classification support
* Larger dataset (2,000+ examples)
* Additional categories (e.g., collaboration, leadership)
* More real-world Slack data integration

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## 👤 Author

Created by Kingston Lewis as part of the Coding in Color program for the AI Dev team.