In Current Directions in Psychological Science
Unfortunately not open access.
Abstract
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.
