Artificial intelligence is shaping education, work, government, healthcare, and public life — yet it is widely misunderstood. Much of what is written about AI focuses either on technical detail inaccessible to most readers, or on exaggerated claims that obscure how these systems actually work.

This textbook takes a different approach.

Written for students, educators, professionals, and policy readers, this book explains artificial intelligence conceptually, not mathematically. It shows what AI systems really do, where their limits lie, and why human judgment, accountability, and ethics remain essential.

Rather than teaching readers how to build AI systems, this book teaches them how to understand, evaluate, and question AI systems already embedded in society.

Inside this book, you will learn:

What people mean — and misunderstand — when they say “AI”
How machine learning works conceptually, without equations or code
Why AI systems rely on pattern recognition rather than understanding
How data shapes outcomes, bias, and fairness across societies
Where AI performs well — and where it fails
Why automation cannot replace human judgment
How AI is used in healthcare, education, work, and public services globally
What AI cannot do, regardless of scale or sophistication
How to evaluate AI claims, headlines, and public narratives critically
How to become AI-literate without becoming an engineer

This book is designed for long-term relevance. It avoids platform-specific tools, vendor examples, and trend-based speculation. Instead, it provides durable concepts that remain useful as technologies evolve.

Ideal for:

Undergraduate and postgraduate students
Foundation and interdisciplinary AI courses
Ethics, sociology, policy, and technology studies
Educators and lecturers
Professionals working alongside AI systems
Readers seeking clarity rather than hype

"Synopsis" may belong to another edition of this title.

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Artificial Intelligence

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