π€ AI Summary
This study addresses the lack of large-scale, localized behavioral analyses concerning the everyday use of generative AI in Australia. Leveraging the WildChat dataset, we employ descriptive statistics and multi-level dialogue classification techniques to systematically examine Australian usersβ linguistic preferences, interaction intents, and work-relatedness. Our analysis reveals, for the first time, a distinctive interaction pattern in this region characterized by execution-oriented prompts and strong alignment with professional tasks, alongside an emerging trend toward multilingual usage and self-expression. By bridging the gap in regional AI behavior research, this work provides critical empirical evidence for understanding the application characteristics of generative AI within local contexts.
π Abstract
Generative AI chatbots are increasingly embedded in everyday life, yet most large-scale studies describe global patterns. This paper presents an Australia-focused analysis of WildChat, a public dataset of real-world ChatGPT interaction logs. Using descriptive analysis and a multi-layer classification scheme, we analysed 37,845 conversations identified as Australian, examining language diversity, work relevance, interaction intent, topic distribution, turn-taking, temporal change, work activities, and Australia-related domains. Our findings show that the Australian subset is strongly action-oriented and comparatively work-oriented, with most interactions classified as doing and a majority of conversations classified as work-related. The dataset also shows multilingual use and a growing presence of self-expression over time. Australia-related conversations frequently invoke local institutions, laws, regulators, education systems, companies, cultural references, and public services. Finally, we outline implications for future research, including local AI evaluation, multilingual participation, context-aware design, and safeguards for everyday high-stakes domains.