The Social Learning of AI: How Human Collective Behavior Accelerates Machine Intelligence through Recursive Feedback

The emergence of large language models represents a pivotal phenomenon for behavioral sciences, demonstrating how machine intelligence is socially constructed through human interaction. This study investigates the social learning mechanisms through which human collective behavior accelerates the development of artificial intelligence's social cognition. We propose a novel theoretical framework positing that AI undergoes a rapid socialization process via a recursive feedback loop with human trainers. To examine this, we employed a mixed-methods approach combining quantitative behavioral analysis with qualitative case study. We analyzed the textual outputs of a language model from its base training through supervised fine-tuning to Reinforcement Learning from Human Feedback, assessing its responses across standardized social scenarios for prosociality, coherence, and normative alignment. Concurrently, we conducted a qualitative examination of the human feedback process, including analysis of rater guidelines and interviews with AI trainers. Quantitative results showed a statistically significant increase in prosocial and normative responses following human feedback training, indicating a clear behavioral shift toward human social standards. Qualitative analysis revealed this shift is driven by a recursive social learning cycle where human raters, as proxies for collective social preferences, systematically reinforce desirable behaviors in the AI. The model internalizes these reinforced patterns and generates new behaviors, which are then evaluated and corrected again, creating a closed-loop socialization system operating at unprecedented scale and speed. This research provides a behavioral science framework for understanding AI development, positioning machines as active participants in a dynamic social system whose intelligence is constructed through recursive behavioral reinforcement, with significant implications for bias mitigation and human–AI ecosystem design.