Course Content
Foundations of Artificial Intelligence and Its Conceptual Framework
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Introduction to Artificial Intelligence
Types of Artificial Intelligence
The Historical Development of Artificial Intelligence Across Five Major Phases
Core Concepts in Artificial Intelligence
Machine Learning and Deep Learning
Generative Artificial Intelligence: Understanding, Application, and Impact
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What Is Generative Artificial Intelligence?
How Do Generative Models Work?
Applications of Generative AI
Generative Artificial Intelligence Across Professional Domains
Artificial Intelligence in Daily Life
Data Awareness and Data Quality in Artificial Intelligence Use
Prompt Engineering
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Prompt Engineering in Artificial Intelligence
The Five Components of an Effective AI Prompt
Real-Life Everyday Examples of Prompt Engineering
Common Mistakes and How to Fix Them
Leadership Skills in the Age of Artificial Intelligence
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AI-Driven Leadership Skills
Methodological Framework for Building Leadership Skills in the Age of Artificial Intelligence
Risks and Security Challenges in Artificial Intelligence
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Artificial Intelligence: Risks and Security Challenges
Adversarial Attacks, Data Distribution Shifts, and the Difficulty of Explaining Models
AI Ethics: A Value-Based Framework for Responsible Use
Making Informed Decisions with Artificial Intelligence
A Methodological Framework for Informed AI-Based Decision-Making
Practical Examples from 2025
Distinguishing Between Accurate and Misleading Information Generated by Artificial Intelligence
Social Responsibility and the Impact of Artificial Intelligence Decisions
Developing Innovation Skills Using Artificial Intelligence
Artificial Intelligence and Sustainable Development
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Direct Impacts on Global Goals
SDGs Most Directly Affected by Artificial Intelligence
Global Principles for Responsible and Trustworthy Artificial Intelligence
The Future of Artificial Intelligence
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Emerging Trends and Transformations
Future AI Scenarios (2030–2040)
International AI Driving License

Types of Artificial Intelligence

1. Narrow Artificial Intelligence (ANI)

Narrow Artificial Intelligence is the most common type and the closest to our everyday applications. It focuses on performing a single task or a limited set of tasks with high efficiency, such as image recognition, translation, or data analysis. It is characterized by its specialization, as it cannot think or learn beyond its predefined scope. ANI relies entirely on programmed algorithms and input data.

The first AI program “Logic Theorist” was developed in 1955 to solve logical problems. Since then, narrow AI has progressed significantly, powering many modern applications such as voice assistants like Siri and Alexa, recommendation systems on platforms like Netflix and YouTube, and advanced language models like ChatGPT, which can generate text and interact intelligently and flexibly with users.

 

2. Artificial General Intelligence (AGI)

AGI represents a more advanced stage where AI can perform any cognitive task a human can do, with abilities to learn, think, solve complex problems, and adapt to unfamiliar situations. It is described as “comprehensive intelligence,” fully simulating human mental capabilities.

 

3. Artificial Superintelligence (ASI)

ASI is a theoretical and futuristic stage in which AI surpasses human intelligence in all areas, including reasoning, creativity, logic, and decision-making. This type could potentially improve itself continuously and accelerate its intelligence growth beyond human comprehension.

 

 

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