This study explores the concept of co-creAItivity, i.e. the nexus of creativity and human-centered artificial intelligence for early childhood education and care. Drawing on recent developments in computational creativity and digital pedagogy, this chapter introduces four developmentally appropriate AI personas Storyteller, Explorer, Playmate, and Mentor designed to support creative engagement in early childhood settings. Each persona is analyzed through key interaction modalities control, feedback, pace, and transparency that structure meaningful human–AI collaboration and aligns with acceded and well-founded ECEC teaching and learning values. By situating these personas within established ECEC practices and theories, this study aims to offer a concrete, ethical framework for designing AI systems that amplify creativity in ECEC. The study concludes with a critical discussion of trade-offs, including risks of bias, over-personalization, and environmental costs, arguing for a co-creative, human-centered vision of AI that supports agency, equity, and exploration from the earliest years.

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Teaching with AI Personas: Designing for Co-CreAItivity in ECEC

  • Zacharias E. Andreadakis

摘要

This study explores the concept of co-creAItivity, i.e. the nexus of creativity and human-centered artificial intelligence for early childhood education and care. Drawing on recent developments in computational creativity and digital pedagogy, this chapter introduces four developmentally appropriate AI personas Storyteller, Explorer, Playmate, and Mentor designed to support creative engagement in early childhood settings. Each persona is analyzed through key interaction modalities control, feedback, pace, and transparency that structure meaningful human–AI collaboration and aligns with acceded and well-founded ECEC teaching and learning values. By situating these personas within established ECEC practices and theories, this study aims to offer a concrete, ethical framework for designing AI systems that amplify creativity in ECEC. The study concludes with a critical discussion of trade-offs, including risks of bias, over-personalization, and environmental costs, arguing for a co-creative, human-centered vision of AI that supports agency, equity, and exploration from the earliest years.