About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

AI/ML Workflows • Browser • 20-30 min

Emotional Support Assistant

An AI companion that builds a knowledge graph from your emotional expressions and provides empathetic support for loneliness and emotional wellbeing.

Open About Demo Read README View Course Path
💙

Hello. I'm here to listen and support you. How are you feeling today?

📊 Emotional State Tracker

How are you feeling right now?

🔗 Connection Graph

Your connections will appear here...

💡 Support Tips

  • Talk regularly with someone you trust
  • Try new hobbies or activities
  • Join community groups or clubs
  • Practice self-care daily

📖 About This Assistant

This assistant builds a knowledge graph from your inputs to provide personalized emotional support. It remembers your feelings and asks follow-up questions to help you feel connected.

Privacy & Ethics

All conversations are stored locally in your browser. No data is sent to external servers. This is a demonstration tool - for real emotional support, please contact a mental health professional.

Standard demo guide

Use this demo in a logical learning sequence

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

What this demo is about

An AI companion that builds a knowledge graph from your emotional expressions and provides empathetic support for loneliness and emotional wellbeing.

Learning objectives

  • Explain the main ai/ml decision that Emotional Support Assistant is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for user intent, sentiment cues, safety boundaries, and referral language
  • Observe how the assistant responds differently to low-risk and high-risk messages

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.