Abstract: This study advances beyond the traditional “human-centered” paradigm of experience research by constructing a theoretical framework of Human-Machine Symbiotic Experience (HSX) that treats the “human-machine assemblage” as the core unit of analysis. It emphasizes that experience does not originate solely from isolated individual cognition, but rather emerges as a relational outcome during continuous interaction between humans and intelligent machines. HSX is characterized by functional coupling, adaptive co-evolution, and value co-creation.
First, at the conceptual level, this study provides a precise definition of HSX. Contrary to conventional views that equate experience with individual subjective feelings, HSX is defined here as a holistic subjective experience formed through the continuous interaction between humans and intelligent systems, reflecting the perceived state and evolution of the human-machine relationship. Meanwhile, the study does not attribute consciousness, emotion, or sentience to machines. Instead, it conceptualizes machine-side components from a functional perspective, referring to observable operational states such as algorithmic response patterns, system feedback structures, and learning trajectories. This definitional choice avoids philosophical and psychological category confusions while offering clear and operational theoretical boundaries for subsequent empirical measurement.
Second, the study proposes a developmental path model for HSX formation based on human-machine matching and co-evolution mechanisms. Three distinct developmental patterns are identified: Model I (unidirectional-passive), Model II (bidirectional-passive), and Model III (bidirectional-active). More importantly, the study introduces the concept of the “experience gap,” arguing that transitions from Model II to Model III are hindered by multidimensional barriers, including cognitive gaps (e.g., expectation violations and increased cognitive load), emotional gaps (e.g., discomfort and distrust induced by the uncanny valley effect), and behavioral gaps (e.g., mismatches between human action rhythms and machine learning rhythms). This framework not only explains why higher-order human-machine symbiosis remains difficult to achieve, but also offers a theoretical foundation for bridging these gaps.
Third, based on the above theoretical construction, the study proposes a multi-dimensional measurement framework for HSX, incorporating both human experiential dimensions and machine functional dimensions. This measurement design provides an analytically rigorous and operational tool for future empirical validation, and establishes a methodological basis for examining the structural properties and causal pathways of HSX.
Finally, at the theoretical level, the study hypothesizes that HSX may generate a “dual-helix effect” on individual and societal outcomes. On one hand, well-balanced HSX may enhance well-being, engagement, learning performance, and value co-creation intentions. On the other hand, imbalanced or excessive HSX may induce negative consequences such as decision dependence, emotional substitution, and ethical risks. Furthermore, these effects are not linear or one-directional; rather, they feed back into and reshape subsequent HSX formation processes, resulting in positive or negative spiral dynamics. The study also identifies key boundary conditions that influence both the formation and effects of HSX, thereby enabling the amplification of positive outcomes and mitigation of potential risks to support the sustainable development of human-machine symbiosis.
In conclusion, this research advances the theoretical system of HSX by: (1) clarifying the conceptual meaning and structural dimensions of HSX, (2) revealing the dynamic emergence and stage-based evolution of HSX, and (3) elucidating the mechanisms and boundary conditions affecting HSX outcomes. These contributions not only address the practical challenges of reconfiguring human-machine relations in the digital intelligence era, but also offer important theoretical support for “AI+” strategic initiatives and enterprise intelligent transformation.

Key words: customer experience, actor experience, human-machine symbiotic interaction, artificial intelligence, digital ecosystem