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Communicative applications of artificial intelligence (AI) have burst into the lives of millions of people through products such as ChatGPT, Midjourney, or Replika, with the ability to generate texts and images and even understand natural language and socialize with humans. This study addresses attitudes toward communicative AI, as well as the sociodemographic and personality factors that are related to its positive perception and usage. For this purpose, we use data from a survey conducted at the end of February 2023 among adult residents in Spain (N = 821). Our results suggest that there is a slight predominance of negative attitudes toward communicative AI, which include cognitive and affective elements such as the perception of dehumanizing aspects and job-related threats, caution, or fear. However, a relevant portion of our respondents is not familiar with communicative AI or generalizes their answers toward other applications of AI. Individuals with higher educational level, greater openness to experience, and lower level of futurephobia show more positive attitudes toward these technologies. In terms of adoption, men, younger people, and those with more favorable attitudes toward AI are more likely to decide to use it, while those scoring higher in agreeableness tend to use it less frequently once they have decided to use it. We discuss these results under the premise that early adopters may play a central role in the attitudes of late majority and laggards, as well as in the future development of communicative AI and its regulatory framework. [ABSTRACT FROM AUTHOR]

Personality research has traditionally relied on questionnaires, which bring with them inherent limitations, such as response style bias. With the emergence of large language models such as ChatGPT, the question arises as to what extent these models can be used in personality research. In this study, ChatGPT (GPT-4) generated 2000 text-based personas. Next, for each persona, ChatGPT completed a short form of the Big Five Inventory (BFI-10), the Brief Sensation Seeking Scale (BSSS), and a Short Dark Triad (SD3). The mean scores on the BFI-10 items were found to correlate strongly with means from previously published research, and principal component analysis revealed a clear five-component structure. Certain relationships between traits, such as a negative correlation between the age of the persona and the BSSS score, were clearly interpretable, while some other correlations diverged from the literature. An additional analysis using four new sets of 2000 personas each, including a set of 'realistic' personas and a set of cinematic personas, showed that the correlation matrix among personality constructs was affected by the persona set. It is concluded that evaluating questionnaires and research hypotheses prior to engaging with real individuals holds promise. • Personality research has largely hinged on the lexical hypothesis and questionnaires. • This study investigated the possibilities of using ChatGPT in personality research. • ChatGPT generated personas and completed personality questionnaires. • Means, component loadings, and scale correlations yielded interpretable results. • We show that the outcomes are sensitive to the types of personas. [ABSTRACT FROM AUTHOR]