Artificial intelligence (AI) is rapidly entering classrooms and educational practices, often in advance of coherent guidelines or shared pedagogical frameworks. This volume proposes a structured roadmap for the integration of AI in teaching and learning, grounded in the revised Bloom’s taxonomy of Anderson and Krathwohl (2001). By mapping cognitive processes (remember, understand, apply, analyze, evaluate, create) against factual, conceptual, procedural, and metacognitive knowledge, the book demonstrates how AI can support each level of learning while preserving the central role of pedagogy. Each chapter develops one dimension of the taxonomy, combining theoretical foundations with practical strategies, including objectives, activities, tool and prompt suggestions, assessment guidance, and case studies. Applications range from adaptive retrieval practice and intelligent tutoring to argument mapping, bias detection, augmented peer review, and human–AI co-design. Ethical considerations, including privacy, bias, and authorship, are addressed throughout. Complementary appendices provide prompt libraries, checklists, quick-reference sheets, a glossary, and an up-to-date annotated bibliography. The volume thus equips educators with both conceptual clarity and operational resources to harness AI for meaningful, responsible, and creative learning.

From remembering to creating. A roadmap for teaching with AI / Maria Ranieri; Gabriele Biagini. - ELETTRONICO. - (2025).

From remembering to creating. A roadmap for teaching with AI

Maria Ranieri;Gabriele Biagini
2025

Abstract

Artificial intelligence (AI) is rapidly entering classrooms and educational practices, often in advance of coherent guidelines or shared pedagogical frameworks. This volume proposes a structured roadmap for the integration of AI in teaching and learning, grounded in the revised Bloom’s taxonomy of Anderson and Krathwohl (2001). By mapping cognitive processes (remember, understand, apply, analyze, evaluate, create) against factual, conceptual, procedural, and metacognitive knowledge, the book demonstrates how AI can support each level of learning while preserving the central role of pedagogy. Each chapter develops one dimension of the taxonomy, combining theoretical foundations with practical strategies, including objectives, activities, tool and prompt suggestions, assessment guidance, and case studies. Applications range from adaptive retrieval practice and intelligent tutoring to argument mapping, bias detection, augmented peer review, and human–AI co-design. Ethical considerations, including privacy, bias, and authorship, are addressed throughout. Complementary appendices provide prompt libraries, checklists, quick-reference sheets, a glossary, and an up-to-date annotated bibliography. The volume thus equips educators with both conceptual clarity and operational resources to harness AI for meaningful, responsible, and creative learning.
2025
Maria Ranieri; Gabriele Biagini
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1434238
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