The rapid diffusion of generative artificial intelligence (GenAI) systems has introduced new forms of human-technology interaction, raising the question of whether sustained engagement gives rise to stable, internalized modes of cognition rather than merely transient efficiency gains. Grounded in the Cognitive Mediation Networks Theory, this study investigates Sophotechnic Mediation, a mode of thinking and acting associated with prolonged interaction with GenAI, and presents a comprehensive psychometric validation of the Sophotechnic Mediation Scale. Data were collected between 2023 and 2025 from independent cross-sectional samples totaling 3,932 adult workers from public and private organizations in the Metropolitan Region of Pernambuco, Brazil. Results indicate excellent internal consistency, a robust unidimensional structure, and measurement invariance across cohorts. Ordinal-robust confirmatory factor analyses and residual diagnostics show that elevated absolute fit indices reflect minor local dependencies rather than incorrect dimensionality. Distributional analyses reveal a time-evolving pattern characterized by a declining mass of non-adopters and convergence toward approximate Gaussianity among adopters, with model comparisons favoring a two-process hurdle model over a censored Gaussian specification. Sophotechnic Mediation is empirically distinct from Hypercultural mediation and is primarily driven by cumulative GenAI experience, with age moderating the rate of initial acquisition and the depth of later integration. Together, the findings support Sophotechnia as a coherent, measurable, and emergent mode of cognitive mediation associated with the ongoing GenAI revolution.
翻译:生成式人工智能(GenAI)系统的快速普及引入了人机交互的新形式,这引发了一个问题:持续的交互是否催生了稳定、内化的认知模式,而非仅仅是暂时的效率提升。本研究基于认知中介网络理论,探讨了与长期使用GenAI相关的思维与行动模式——智能技术中介,并对《智能技术中介量表》进行了全面的心理测量学验证。数据采集于2023年至2025年间,样本来自巴西伯南布哥大都市区公共和私营机构的3,932名成年工作者,构成多个独立的横断面样本。结果显示,该量表具有优异的内部一致性、稳健的单维结构以及跨群组的测量不变性。序数稳健验证性因子分析和残差诊断表明,较高的绝对拟合指数反映的是微小的局部依赖性,而非维度设定错误。分布分析揭示了一种随时间演变的模式:非采用者比例持续下降,而采用者群体则趋近于近似高斯分布;模型比较支持双过程跨栏模型优于删截高斯模型设定。实证表明,智能技术中介与超文化中介存在本质区别,其主要驱动力是累积的GenAI使用经验,而年龄则调节了初始习得速度和后期整合深度。综合来看,本研究支持将“智能技术性”视为一种与当前GenAI革命相关的、连贯、可测量且正在涌现的认知中介模式。