Recent advancements in generative AI, such as ChatGPT, have dramatically transformed how people access information. Despite its powerful capabilities, the benefits it provides may not be equally distributed among individuals, a phenomenon referred to as the digital divide. Building upon prior literature, we propose two forms of digital divide in the generative AI adoption process: (i) the learning divide, capturing individuals' heterogeneous abilities to update their perceived utility of ChatGPT; and (ii) the utility divide, representing differences in individuals' actual utility derived from per use of ChatGPT. To evaluate these two divides, we develop a Bayesian learning model that incorporates heterogeneities in both the utility and signal functions. Leveraging a large-scale clickstream dataset, we estimate the model and find significant learning and utility divides across various social characteristics. Interestingly, individuals without any college education, non-white individuals, and those with lower English literacy derive larger utility gains from ChatGPT, yet update their beliefs about its utility at a slower rate. Furthermore, males, younger individuals, and those in occupations with greater exposure to generative AI not only obtain higher utility per use from ChatGPT but also learn about its utility more rapidly. Besides, we document a phenomenon termed the belief trap, wherein users underestimate ChatGPT's utility, opt not to use the tool, and thereby lack new experiences to update their perceptions, leading to continued underutilization. Our simulation further demonstrates that the learning divide can significantly affect the probability of falling into the belief trap, another form of the digital divide in adoption outcomes (i.e., outcome divide); however, offering training programs can alleviate the belief trap and mitigate the divide.

翻译：近期生成式人工智能（如ChatGPT）的突破性进展，极大地改变了人们获取信息的方式。尽管其功能强大，但其带来的益处可能并未在个体间均等分配，这一现象被称为数字鸿沟。基于已有文献，我们提出了生成式人工智能采纳过程中的两种数字鸿沟形式：（i）学习鸿沟，反映个体在更新其对ChatGPT感知效用方面的异质能力；（ii）效用鸿沟，代表个体每次使用ChatGPT所获实际效用的差异。为评估这两种鸿沟，我们构建了一个贝叶斯学习模型，该模型同时纳入了效用函数与信号函数的异质性。利用大规模点击流数据集，我们对模型进行了估计，发现不同社会特征群体间存在显著的学习鸿沟与效用鸿沟。有趣的是，未受过高等教育的个体、非白人族裔以及英语读写能力较低者从ChatGPT中获得了更大的效用增益，但其对ChatGPT效用的信念更新速度较慢。此外，男性、年轻群体以及职业中接触生成式人工智能机会更多的人群，不仅每次使用ChatGPT获得更高效用，而且对其效用的学习速度也更快。同时，我们记录了一种称为“信念陷阱”的现象：用户低估ChatGPT的效用，选择不使用该工具，从而缺乏更新其认知的新体验，导致持续的低度使用。我们的模拟进一步表明，学习鸿沟会显著影响陷入信念陷阱的概率，这是采纳结果中数字鸿沟的另一种表现形式（即结果鸿沟）；然而，提供培训项目能够缓解信念陷阱并缩小这种鸿沟。