The performance of large language models (LLMs) is often compared to human performance on decision-making tasks. In this Review, Brady and colleagues examine LLM outputs through the lens of dual-process theory, considering the hallmarks of System 1 and System 2 human decision-making.

Complex decision-making tasks can be addressed with artificial intelligence (AI) systems that complement human capabilities. In this Perspective, Gonzalez and Heidari provide a conceptual framework to integrate human and AI decision-making, highlighting technical challenges and ethical risks.

Machine learning algorithms can increase the effectiveness of mental health interventions, but biased systems might exacerbate current disparities in case identification and treatment. In this Perspective, Timmons et al. propose a model that integrates artificial intelligence methods with co-creation techniques to reduce bias and increase inclusion in mental healthcare.

Generative artificial intelligence (AI) is increasingly being integrated into education, where it can boost learners’ performance. However, these uses do not promote the deep cognitive and metacognitive processing that are required for high-quality learning.

Decades of research has aimed to distinguish cognitive control from bottom-up learning and memory processes. Despite early consensus that both mechanisms contribute to key phenomena, debates persist. We argue that battles like these reach a point of diminishing returns, consuming substantial time, effort, and resources without further advancing theoretical understanding.

This month we introduce Consensus Statement articles, which present formally derived expert recommendations for a research field, methodological approach or clinical practice.