Social need fulfillment model for human–AI relationships.

Abstract

Humans have diverse social needs (e.g., security, relatedness), which when fulfilled can produce concrete (e.g., pleasure, satiation of a need) and symbolic outcomes (e.g., what this instance of need fulfillment means about one's relationship with the source of fulfillment). Advances in large language models have exponentially increased the potential that intelligent agents hold for social need fulfillment. Although some evidence shows that modern intelligent agents can meet or facilitate social need fulfillment, other sources suggest that fulfillment generated by intelligent agents is less effective than that generated by humans. In this review, we introduce a model of social need fulfillment in human–artificial intelligence relationships, which proposes that the majority of humans process most of their interactions with intelligent agents automatically at first and then quickly engage in deliberative processing. In these cases, we expect only concrete outcomes of need fulfillment are obtained, as the human rationally considers that machines cannot care about them. However, in some situations (e.g., lonely human, responsive agent), deliberative processing may be bypassed, and both concrete and symbolic outcomes may be obtained, mirroring what occurs in human–human interactions. In these cases, going forward, we expect both types of outcomes will continue to be available from interactions with intelligent agents. We close with a big-picture analysis of the potential that artificial intelligence holds to meet human social needs, including its promise and potential pitfalls. (PsycInfo Database Record (c) 2025 APA, all rights reserved)