Generative AI in Education: Pedagogy and Research: A Practical Guide for the AI-Enhanced Educational Landscape

Name: Generative AI in Education: Pedagogy and Research: A Practical Guide for the AI-Enhanced Educational Landscape
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Bol This monograph examines the paradigm shift triggered by generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) within educational contexts. This monograph examines the paradigm shift triggered by generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) within educational contexts. It systematically investigates how these technologies redefine core pedagogical processes, ranging from instructional design and personalized tutoring to assessment and scholarly inquiry. A primary contribution of this work is the synthesis of technical, pedagogical, and critical discourses into a cohesive framework. By mapping the capabilities and constraints of generative AI onto established learning paradigms, including Constructivism, Cognitive Load Theory, and Connectivism, the text provides a robust theoretical foundation for technology integration. Furthermore, it operationalizes “Critical AI Literacy” as a fundamental competency, offering empirical methodologies for prompt engineering, assessment redesign, and the rigorous evaluation of machine-generated outputs. Ultimately, the book outlines a structural roadmap for responsible AI adoption, balancing applied pedagogical innovation with an in-depth examination of the underlying ethical, equity, and professional challenges.

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This monograph examines the paradigm shift triggered by generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) within educational contexts. This monograph examines the paradigm shift triggered by generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) within educational contexts. It systematically investigates how these technologies redefine core pedagogical processes, ranging from instructional design and personalized tutoring to assessment and scholarly inquiry. A primary contribution of this work is the synthesis of technical, pedagogical, and critical discourses into a cohesive framework. By mapping the capabilities and constraints of generative AI onto established learning paradigms, including Constructivism, Cognitive Load Theory, and Connectivism, the text provides a robust theoretical foundation for technology integration. Furthermore, it operationalizes “Critical AI Literacy” as a fundamental competency, offering empirical methodologies for prompt engineering, assessment redesign, and the rigorous evaluation of machine-generated outputs. Ultimately, the book outlines a structural roadmap for responsible AI adoption, balancing applied pedagogical innovation with an in-depth examination of the underlying ethical, equity, and professional challenges.

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