Session 1: What is AI?

Goal: Introduce fundamental AI concepts, types, and their applications.
Key Points:
- Definition of AI (Narrow, General, and Super AI)
- Key AI technologies (Machine Learning, Deep Learning, NLP)
- Real-world examples of AI in different industries

Hands-on: Experimenting with AI-generated text using ChatGPT & Copilot

Goal: Understand ethical concerns, bias, and the importance of responsible AI development.

• AI bias and its real-world impact
• Ethical AI decision-making (privacy, surveillance, misinformation)
• Case studies of biased AI systems (e.g., biased hiring tools, racial/gender bias in facial recognition)
• The role of inclusion & diversity in AI development
•  Hands-on Activity:
• Discussion: Identifying bias in AI-generated outputs (ChatGPT, Copilot)
• Group brainstorm: How can AI be more inclusive?

Goal: Familiarize with no-code AI tools for AI model development.

• Overview of Google AutoML, Teachable Machine, Lobe.ai
• No-code AI vs. traditional coding approaches
• Use cases and limitations of no-code AI tools
• Hands-on: Setting up a project in Google AutoML

Session 4: Exploring No-Code AI Platforms

Session 4: Exploring No-Code AI Platforms

Goal: Familiarize with no-code AI tools for AI model development.
Key Points:

• What makes good training data?
• Cleaning and labeling data
• Avoiding data bias
• Hands-on:
  • Uploading and labeling data in Lobe.ai.
  • Explore datasets in Kaggle.com, sigma.ai, huggingface.cp

Goal: Understand the importance of high-quality data for AI models.

• Basics of computer vision and image recognition
• Training a model with labeled images
• Testing and improving AI predictions
• Hands-on: Train an image classification model using Teachable Machine

• Uploading and labeling data in Lobe.ai.
• Explore datasets in Kaggle.com, sigma.ai, huggingface.cp

Goal: Learn how AI processes human language.

• Introduction to NLP (text recognition, sentiment analysis)
• Chatbots and virtual assistants
• AI-powered language translation
•  Hands-on: Explore a chatbot using one of options:
  • ChatGPT API playground (no-code version)
  • Microsoft Power Virtual Agents
  • Google Dialogflow (Advanced No-Code NLP Chatbot)
  • Landbot.io (Best for Interactive Chatbot Flows)
• Hands-on: Train an image classification model using Teachable Machine

Goal: Learn how AI uses different types of data.

• Data types (structured vs. unstructured)
• Importance of data in AI model training
• Data-driven decision-making
•  Hands-on: Analyzing AI-generated insights with Microsoft Copilot

Goal: Explore how AI enhances creativity in design and content generation.
Key Points:
- AI in art, music, and content creation
-  AI-generated visuals, logos, and presentations
- Ethical concerns of AI-generated media
- Hands-on: Create an AI-generated infographic using Canva AI

Session 9: Understanding Data – Structured vs. Unstructured

Goal: Learn how AI uses different types of data.
Key Points:
- Data types (structured vs. unstructured)
- Importance of data in AI model training
- Data-driven decision-making
- Hands-on: Analyzing AI-generated insights with Microsoft Copilot

Goal: Understand ML concepts and how machines learn from data.
Key Points:
- Supervised vs. unsupervised learning
- AI model training process
- Common ML algorithms
-  Hands-on: Training a basic model using Google Vertex AI (no-code)

Goal: Evaluate AI model performance.
Key Points:
- Accuracy, precision, recall, and F1-score
- How to improve model performance
- Overfitting vs. underfitting
- Hands-on: Testing AI predictions with Lobe.ai

Goal: Learn how to identify, measure, and mitigate bias in AI models.
Key Points:
- How bias enters machine learning models (dataset bias, algorithmic bias)
- Techniques for bias detection and mitigation
- Fairness metrics (Demographic Parity, Equalized Odds)
- Regulations & frameworks (AI Act, Responsible AI Principles)
- Hands-on Activity:

• Testing AI fairness using Google AutoML / Vertex AI
• Adjusting datasets and analyzing results

Session 13: Basics of Python for AI

Goal: Learn how to use Jupyter Notebooks for AI research.

• What is Jupyter Notebook?
• Running AI experiments
• Visualizing data
• Hands-on: Running basic Python scripts in Google Colab

(Expanded with financial modeling case study & exercise as discussed)


• AI-driven financial modeling, risk analysis, and forecasting
• AI in fraud detection and automated stock trading
• Hands-on financial modeling using Microsoft Copilot, Google Sheets AI, and ChatGPT

Goal: Train a basic AI model with Python.
Key Points:
- Training an ML model using Scikit-Learn
- Understanding model accuracy
- Saving and deploying models
-  Hands-on: Training a small dataset model in Python

Session 16: AI in IoT & Smart Devices

Goal: Understand how AI enhances the Internet of Things (IoT) for automation and decision-making.
Key Points:

- AI-powered IoT applications (smart homes, wearables, industrial automation)
- Real-time data processing in IoT devices (AI-powered voice assistants)
- AI’s role in cybersecurity for IoT (anomaly detection)

- Case Study:

• Smart Cities & AI: How AI optimizes energy consumption and reduces waste
• Amazon Alexa / Google Assistant: Understanding AI-based speech recognition

-  Hands-On:

• Simulating AI-powered automation using IFTTT (If This, Then That) for smart devices
• Exploring real-time AI insights in IoT data

(Expanded with financial modeling case study & exercise as discussed)

- AI-driven financial modeling, risk analysis, and forecasting
- AI in fraud detection and automated stock trading
- Hands-on financial modeling using Microsoft Copilot, Google Sheets AI, and ChatGPT

Goal: Explore how AI contributes to sustainability, environmental protection, and social good.
Key Points:
-  AI in climate change research, renewable energy optimization, waste management
- AI’s impact in disaster response, poverty reduction, and healthcare access

- Case Study:

• AI for Wildlife Conservation (Google’s AI detecting endangered species)
• AI in Renewable Energy Forecasting

- Hands-On:

• Brainstorming Exercise: How can AI solve local environmental/social problem

Session 19: AI Career Paths & Job Market Trends

Goal: Help participants create an AI portfolio to showcase their skills.

• Importance of an AI portfolio for job applications
• Showcasing no-code AI projects, case studies, and reports Using LinkedIn, GitHub, Medium for AI branding

Hands-On:

• Create an AI-powered resume using ChatGPT
• Develop a mini AI portfolio with case studies (Google Docs/Notion)

Goal: Help participants create an AI portfolio to showcase their skills.
Key Points:
- Importance of an AI portfolio for job applications
- Showcasing no-code AI projects, case studies, and reports
- Using LinkedIn, GitHub, Medium for AI branding

Hands-On:

• Create an AI-powered resume using ChatGPT
• Develop a mini AI portfolio with case studies (Google Docs/Notion)

Goal: Promote inclusivity and highlight women leaders in AI.
Key Points:
- The importance of diversity in AI
- Challenges and opportunities for women in AI
- Inspirational case studies (e.g., Mira Murati, Fei-Fei Li, Joy Buolamwini)

Panel Discussion:

• Guest talk with a female AI leader (if possible)
• Interactive Q&A session

Session 22: Final Quiz

Age: 16+
​Citizen of the Republic of Kosovo
Language: Albanian, English
​Preference: Students/Graduates from Vocational​ Secondary Schools