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Toward Complementary Intelligence: Integrating Cognitive and Machine AI
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AI
DOI：10.1177/09637214251407571.png)
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This article calls for complementary human-AI intelligence. Rather than redefining intelligence to fit machine capabilities, we argue for designing AI that complements and extends human cognition. We distinguish between
cognitive AI
, which is grounded in cognitive science to model human perception, learning, and decision-making, and
machine AI
, which achieves large-scale performance through data-driven optimization. Building on advances in machine learning alignment and human-AI complementarity, we propose an integrative framework that connects cognitive and machine AI across four routes:
embedding integration
, aligning human and machine representations;
instruction encoding
, using machine AI to translate goals into cognitive AI;
training agents
, using cognitive AI to guide and train machine AI through human-like data; and
coevolving agents
, enabling cognitive and machine AI to coadapt and improve together over time. These integration routes provide a foundation for
complementary intelligence
: systems that combine human interpretability with machine scalability and precision to enhance trust, adaptability, and human agency in complex sociotechnical environments.
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