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The integration of Artificial Intelligence (AI) in education presents opportunities and challenges for developing critical thinking and transversal competencies. While AI tools can enhance learning, research shows they can lead to cognitive offloading and dependency. This paper presents CARE+ (Responsible and Experiential Augmented Cognition), a neurologically informed methodology that strategically alternates between AI-assisted and independent learning phases. Based on EEG research on cognitive adaptation to AI use, validated pedagogical models (CARE-KNOW-DO, LAMB), and UNESCO AI competency frameworks, CARE+ preserves cognitive autonomy while developing 21st century skills. We demonstrate the methodology through empirical validation with culturally sensitive software requirements engineering and provide quantitative metrics for evaluation. Our approach offers a systematic framework for integrating AI in education while maintaining human agency at the center of the learning process.

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CARE+: A Methodology for AI-Enhanced Learning that Develops 21st Century Competencies

  • 会议论文

  • 发表日期：

    2026年

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  • Charlie Cárdenas Toledo,
  • Fernanda Maricela Soto Guerrero,
  • María Isabel Loaiza

摘要

The integration of Artificial Intelligence (AI) in education presents opportunities and challenges for developing critical thinking and transversal competencies. While AI tools can enhance learning, research shows they can lead to cognitive offloading and dependency. This paper presents CARE+ (Responsible and Experiential Augmented Cognition), a neurologically informed methodology that strategically alternates between AI-assisted and independent learning phases. Based on EEG research on cognitive adaptation to AI use, validated pedagogical models (CARE-KNOW-DO, LAMB), and UNESCO AI competency frameworks, CARE+ preserves cognitive autonomy while developing 21st century skills. We demonstrate the methodology through empirical validation with culturally sensitive software requirements engineering and provide quantitative metrics for evaluation. Our approach offers a systematic framework for integrating AI in education while maintaining human agency at the center of the learning process.