Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model
Julia Mossbridge

TL;DR
This paper proposes a new dynamic relational model where AI acts as a learning partner, evolving through human interactions to foster ethical, adaptive, and synergistic human-AI relationships for complex problem-solving.
Contribution
It introduces the Dynamic Relational Learning-Partner (DRLP) model, emphasizing AI as an adaptive, cooperative partner that learns from humans and fosters a new paradigm in human-AI collaboration.
Findings
AI can evolve through human interactions and feedback.
A hybrid intelligence emerges from human-AI heterogeneity.
Design interventions enhance adaptive and ethical relationships.
Abstract
As artificial intelligence (AI) continues to evolve, the current paradigm of treating AI as a passive tool no longer suffices. As a human-AI team, we together advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. Drawing from interdisciplinary concepts such as ecorithms, order from chaos, and cooperation, we explore how AI can evolve and adapt in unpredictable environments. Arising from these brief explorations, we present two key recommendations: (1) foster ethical, cooperative treatment of AI to benefit both humans and AI, and (2) leverage the inherent heterogeneity between human and AI minds to create a synergistic hybrid intelligence. By reframing AI as a dynamic partner, a model emerges in which AI systems develop alongside humans, learning from human interactions and feedback loops including reflections on team…
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Taxonomy
TopicsCognitive Science and Mapping
