In this chapter, we explore how technology can enhance human cognition in ways that foster lasting development. Central to our discussion is the concept of the Meta-Augmented Human, an approach that aims to permanently amplify cognitive abilities by cultivating skills that transcend specific tools or interfaces. Our exploration covers sensing technologies that detect learning states, actuation systems that provide timely support, and AI-driven knowledge transfer mechanisms. We examine how smart sensors can recognize cognitive and affective states to deliver personalized assistance that builds lasting capabilities rather than creating dependency. The chapter addresses key challenges in this emerging field, including accurately recognizing cognitive/affective states, and designing interventions that strike the right balance between immediate help and long-term skill development. We conclude by examining future directions for human–AI collaboration in learning environments, highlighting the importance of technologies that enhance human potential while preserving autonomy and fostering skills that remain even when the technology is absent.

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Beyond the Interface: Augmenting Meta-Skills for Long-Term Cognitive Enhancement

  • Shoya Ishimaru

摘要

In this chapter, we explore how technology can enhance human cognition in ways that foster lasting development. Central to our discussion is the concept of the Meta-Augmented Human, an approach that aims to permanently amplify cognitive abilities by cultivating skills that transcend specific tools or interfaces. Our exploration covers sensing technologies that detect learning states, actuation systems that provide timely support, and AI-driven knowledge transfer mechanisms. We examine how smart sensors can recognize cognitive and affective states to deliver personalized assistance that builds lasting capabilities rather than creating dependency. The chapter addresses key challenges in this emerging field, including accurately recognizing cognitive/affective states, and designing interventions that strike the right balance between immediate help and long-term skill development. We conclude by examining future directions for human–AI collaboration in learning environments, highlighting the importance of technologies that enhance human potential while preserving autonomy and fostering skills that remain even when the technology is absent.