Recent evidence reported by Tully, Longoni, and Appel (2025) suggests that lower artificial intelligence (AI) literacy predicts greater receptivity toward AI. We revisit this claim using the public data from Study 3 of that article, which measures past usage of five AI tool categories on a five-point frequency scale. We first reproduce the negative association between AI literacy and aggregate AI usage using OLS on participant-level averages, binary logit, ordered logit, and multinomial logit specifications. We then show that the aggregate relationship masks substantial heterogeneity by tool type. In our demographic-adjusted primary specification, AI literacy does not significantly predict text AI usage (ordered-logit $β$ = -0.090, p = .387), whereas it remains a strong predictor of non-text AI adoption ($β$ = -0.377, p < .001). The non-text effect is also robust under Tully et al.'s original Study 3 control specification ($β$ = -0.502, p < .001). Binary, ordered-logit, and multinomial specifications suggest that the non-text relationship is primarily an adoption/non-adoption pattern rather than evidence of intensive use: the demographic-adjusted odds ratio of ever having used a non-text AI tool is 0.68. Thus, in the study that measures self-reported past usage rather than stated preferences, the evidence does not support a simple claim that lower AI literacy predicts greater receptivity to AI in general. It points instead to a narrower pattern of broader adoption across lower-penetration, non-text AI tools.

翻译：Tully、Longoni和Appel（2025）近期报告的证据表明，较低的人工智能素养预示着对AI更高的接受度。我们利用该文章研究3的公开数据重新审视了这一观点，该研究以五点频率量表测量了五类AI工具的历史使用情况。我们首先通过参与者层面均值的OLS、二元Logit、有序Logit和多项Logit模型，复现了AI素养与聚合AI使用之间的负相关性。随后发现，聚合关系掩盖了工具类型间的显著异质性。在经人口学调整的主要规范中，AI素养对文本AI使用无显著预测作用（有序Logit β=-0.090，p=0.387），而其对非文本AI采用仍具有强预测性（β=-0.377，p<0.001）。在Tully等人原始研究3的控制规范下，非文本效应同样稳健（β=-0.502，p<0.001）。二元、有序Logit和多项规范表明，非文本关系主要表现为采用/未采用模式而非密集使用证据：经人口学调整后，曾使用非文本AI工具的比值比为0.68。因此，在测量自报历史使用而非陈述性偏好的研究中，证据并不支持"较低AI素养预示着对AI整体更高接受度"的简单论断，而是指向一种更窄的模式——即低渗透率非文本AI工具的广泛采用。