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As part of a larger project, this paper reports on preliminary findings of a study exploring use cases of ChatGPT and associated behaviors and experiences among users of an online forum. Posts on a ChatGPT‐related forum on Reddit (n = 452) were analyzed using qualitative content analysis. This paper reports on themes relevant to this study, including the types of tasks for which users used ChatGPT, user experiences, and perceived impacts of ChatGPT. ChatGPT was often used to facilitate various writing tasks (e.g., writing an essay), academic tasks (e.g., finding scientific references for a research paper), everyday tasks (e.g., creating a meal plan), and conversational purposes (e.g., having a simulated conversation about a past event). Users expressed positive (e.g., excited, amazed) and negative (e.g., fooled, concerned) feelings toward the technology. They raised various issues and problems with ChatGPT at the content (e.g., inaccuracy, incompletes) and system (e.g., unavailability, instability) levels. Users discussed the perceived impacts of ChatGPT on individuals (e.g., unemployment) and society (e.g., AI divide). Study findings can inform the design of policies and guidelines for mitigating AI problems and promoting the effective and ethical use of emerging AI technologies. [ABSTRACT FROM AUTHOR]

Author keywords attached to academic papers are often used in intellectual structure analysis. However, the length and selection criteria for keywords vary across publications and, even some publishers do not require keywords for their articles. To explore the opportunity to overcome such keyword inconsistency issues, this study compared author keywords from papers focused on the digital divide with those extracted using the language models, BERT and ChatGPT. Preliminary findings reveal structural variations across the keyword networks and suggest a potential need to revisit keyword‐based research. Future research will expand the scope of the dataset and conduct an in‐depth analysis of keyword patterns across the language models. [ABSTRACT FROM AUTHOR]

In this short paper, we employ a month‐long autoethnography to investigate the utilization of ChatGPT through metaphor analysis. We conceptualize three metaphors—unreliable narrator, court jester, and sounding board—that possess the most explanatory capabilities in describing what ChatGPT is, when it can be used, and how it can be helpful. We posit that grounding the use of ChatGPT in metaphors could facilitate discussions and streamline the intricate mechanism of Large Language Models (LLMs). Our study indicates that by proffering playful metaphors as substitutes to apocalyptic and arcane ones, we can enhance the accessibility and comprehensibility of ChatGPT for non‐experts and policymakers, thereby potentially contributing to more informed and productive dialogues about the role and potential of LLMs in everyday life. [ABSTRACT FROM AUTHOR]

Recently, the scholarly community has been eagerly exploring how AI‐produced content should be integrated into both academic writing and scholarly publishing. This paper investigates the prevailing responses to the introduction of ChatGPT in November 2022 and the interest that has been afforded it by both the academy and the publishing industry. A review of the published literature on aspects of ChatGPT authorship was carried out, finding that government and the publishing industry have unequivocally asserted that large language models (LLMs) like ChatGPT do not possess the traits of a person and are not able to author texts as a result. Other approaches, including practice, have been less vehement. To assess the integration of instructions on referencing ChatGPT using APA, top Google hits in the.edu domain were collected and analyzed over a 6‐week period from March 14 to April 18, 2023, a time during which official recommendations of the APA Style were finalized. Findings reveal that librarians were quick to provide guidance, but slow to update that guidance, contributing to the potential for misunderstanding the affordances of and best practices for work with LLMs. [ABSTRACT FROM AUTHOR]