SYNC (Synergistic Yield of Networked Co-evolution): Advancing Human–AI Teamwork for Human Well-being

Lixiao Huang

Track: long paper (up to 10 pages)

Keywords: human emotional attachment, human–AI co-evolution, human–AI co-learning, human well-being

TL;DR: This paper explores human–AI co-evolution, with the goal of achieving human well-being via co-learning, informed by human factors principles.

Abstract: As human–artificial intelligence (AI) collaborations become increasingly prevalent, understanding the coevolutionary dynamics between humans and AI is critical. Human–AI coevolution includes: (a) how humans evolve as they explore and learn about themselves and the world across the lifespan; (b) how AI systems evolve through improvements in software, hardware, interfaces, and interaction processes; and (c) how humans and AI systems influence one another and adapt together through ongoing interaction. This process may occur at the individual level—from brief interactions to full lifecycles—and at the species level over generations. This coevolutionary process can foster emotional attachment, leading to human well-being, which depends on AI's alignment with an individual's self-concept. This study introduces the Generalized Human Emotional Attachment (GHEA) framework, which offers new insights for designing human-centered systems and optimizing human–AI–robot teaming for improved individual well-being and team outcomes. The GHEA model applies to any entity, including AI systems, regardless of physical embodiment. Human–AI co-learning—through self-concept development, alignment of AI attributes, and the promotion of best practices—can foster emotional attachment to AI. This paper reviews the literature from a human factors perspective and proposes a framework for designing and evaluating human-centered systems and human–AI–robot teaming dynamics, which are critical for promoting well-being and effective collaboration.

Submission Number: 30