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.
Gonzalez et al. (Mon,) studied this question.