🤖 AI Summary
This study addresses the lack of systematic theoretical frameworks accounting for the pronounced individual differences observed in user interactions with AI chatbots. It pioneers the extension of Bronfenbrenner’s bioecological systems theory into the domain of personalized artificial intelligence, proposing a user-centered heuristic analytical framework that integrates multilevel contextual factors. At its core, the framework conceptualizes the reciprocal, iterative, and co-adaptive exchanges between users and memory-endowed AI agents as a dynamic developmental process. Through theoretical modeling, human–AI interaction analysis, and contextual systems mapping, the project formulates a structured explanatory model of variability, elucidating the mechanisms underlying the co-evolution of human–AI relationships. This approach offers both theoretical grounding and practical insights for the design and study of adaptive, context-aware AI systems.
📝 Abstract
People respond to artificial intelligence chatbots (AICs) in highly variable ways. In this paper, we adapt Bronfenbrenner's theory into a heuristic framework for understanding this variation. The framework places the human user at the center while also placing the AI there and reconceptualizing the proximal processes as the repeated, reciprocal, and coadaptive interactions between the user and a personalized AIC. The surrounding systems identify the contextual factors that shape how the user experiences, interprets, responds to, and is changed by these interactions. Because stateful AICs learn from accumulated exchanges with their users and have memory, users are responding not only to an AIC but also to a version of the AIC that their own prior interactions have helped create. This extension preserves Bronfenbrenner's emphasis on proximal processes while accounting for the unique dynamics of personalized AICs. The resulting framework provides a structured map of where and how variation in human and AIC relationships arises, as well as having implications for researchers, practitioners, and AIC designers.