Factors Shaping Perceptions of AI Tools Among a Nationally Representative Sample of US Adults
Abstract
Individuals throughout the life span are increasingly faced with challenging decisions regarding the adoption of generative AI tools in a variety of workplace contexts. In this nationally representative study of N = 500 US adults collected via the Prolific platform, we examined how a variety of demographic factors, thinking dispositions, and industry-types, including both K-12 and higher education (N = 37), influenced how individuals considered risk and utility of generative AI tools in their work. AI-relevant scales included the General Attitudes towards AI scale, an AI risk and benefits scale, AI frequency and expertise, and a scale for the assessment of non-experts’ AI literacy. While higher education and K-12 industry status was not linked to differences in differential AI perceptions, age was closely linked to a variety of outcomes, including perceived benefits of AI, perceived risk of AI, and the optimal role of AI in workplace applications. For example, older individuals were in some cases more likely to agree strongly with statements emphasizing the potential benefits of AI and were somewhat less likely to agree with statements emphasizing the risks of AI in workplace contexts. Further analyses identified nuanced links with thinking dispositions including one’s likelihood to engage in cognitively challenging activities and how susceptible one was to everyday cognitive failures. These findings may have implications for future curricula and programming designed to help individuals throughout the life span manage the proliferation of generative AI tools in workplace contexts, including within gerontology education.
Study specs
A nationally representative survey of US adults conducted via the Prolific platform using various AI-relevant scales, including attitudes, risks, benefits, frequency of use, expertise, and literacy assessments.
- Institution
- Virginia Tech
- Discipline
- Behavioral Science
- Sample Size
- N=500
- Study Type
- Survey Research
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Demographic factors, industry types, thinking dispositions, and attitudes toward generative AI tools, including risk and utility perceptions.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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