Stefan M. Herzog

Ezequiel Lopez-Lopez, Christoph M. Abels, Philipp Lorenz-Spreen et al.

People navigate complex environments using cues, heuristics, and other strategies, which are often adaptive in stable settings. However, as AI increasingly permeates society's information environments, those become more adaptive and evolving: LLM-based chatbots participate in extended interaction, maintain conversational histories, mirror social cues, and can hypercustomize responses, thereby shaping not only what information is accessed but how questions are framed, how evidence is interpreted, and when action feels warranted. Here we propose a framework for sustained human-AI interaction that rests on invariant features of human cognition and human--AI interaction and centers on three interlinked phenomena: entanglement between users and AI systems, the emergence of cognitive and behavioral drift over repeated interactions, and the role of metacognition in the awareness and regulation of these dynamics. As conversational agents provide cues (e.g., fluency, coherence, responsiveness) that people treat as informative, subjective confidence and action readiness may increase without corresponding gains in epistemic reliability, making drift difficult to detect and correct. We describe these dynamics across micro-, meso-, and macro-levels. The framework identifies four metacognitive intervention points and psychologically informed interventions that provide metacognitive scaffolding (boosting and self-nudging). Finally, we outline a long-horizon research agenda for scientific foresight.

Julian Berger, Jason W. Burton, Ralph Hertwig et al.

The collaboration between humans and artificial intelligence (AI) holds the promise of achieving superior outcomes compared to either acting alone-a phenomenon called human-AI synergy. Nevertheless, our understanding of the conditions that facilitate such human-AI synergy when humans are advised by AI remains limited. A recent meta-analysis showed that, on average, human-AI combinations do not outperform the better individual agent. We argue that this pessimistic conclusion arises from insufficient attention to human learning in the experimental designs. To substantiate this claim, we re-analyzed all 74 studies included in the original meta-analysis, yielding two new findings. First, most previous research overlooked design features that foster human learning, such as providing outcome feedback to participants. Second, our re-analysis demonstrated that studies providing outcome feedback show tentatively higher synergy than those without outcome feedback. Crucially, feedback paired with AI explanations tends to yield positive synergy, while explanations without feedback were linked to negative synergy-indicating that explanations increase synergy only when humans can learn to verify the AI's reliability through feedback. We conclude that the current literature underestimates the potential of human-AI collaboration because it predominantly relies on paradigms that do not facilitate human learning, thus hindering humans from effectively adapting their collaboration strategies. We therefore advocate for a paradigm shift in human-AI interaction research that explicitly addresses human learning and thus enhances our understanding of and support for successful human-AI collaboration.