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Artificial intelligence is already changing how students read, write, solve problems, prepare assignments, and demonstrate what they know. The real question is no longer whether AI belongs in education, but how education must change now that cognitive work can be assisted, accelerated, or partially externalized.
Artificial Intelligence in Education examines that transformation with a clear argument: AI should neither be celebrated uncritically nor resisted nostalgically. It must be governed through sound pedagogy, rigorous assessment, professional judgment, and institutional responsibility.
The book explores what it still means to understand when correct execution is no longer sufficient evidence of learning. It analyzes the limits of traditional assessment, the risks of confusing plausible output with genuine understanding, and the growing importance of problem formulation, interpretation, validation, and intellectual responsibility.
It also introduces the concept of augmented modeling as a framework for rethinking learning in contexts where formal execution may be supported by digital tools, while conceptual control and accountability remain nondelegable.
Across chapters on assessment, AI tutoring, engineering, medicine, law, nursing, teacher education, and institutional governance, the book shows how AI affects different professional fields and why each must redefine what students are expected to understand, justify, and defend.
Written for university instructors, secondary educators, academic leaders, institutional decision-makers, and professionals interested in the future of education, this volume offers a rigorous and practical framework for responding to AI without lowering standards or surrendering human judgment.
The central challenge is not technological adoption. It is educational redesign.