Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Exploring the utilisation of ChatGPT in academic libraries: a self-reflection perspective
3
Zitationen
1
Autoren
2024
Jahr
Abstract
Rationale of Study – Exploring the utilisation of ChatGPT in academic libraries, with a focus on ChatGPT's views, not only addresses immediate concerns within the library community but also advances an understanding of the evolving relationship between artificial intelligence and information services in contemporary society.Methodology – The study focused on the unique perspectives or viewpoints of ChatGPT itself. The main objective of the study was to explore ChatGPT's selfawareness and understanding of its role within the academic library context, including its perception of challenges, limitations, and contributions in facilitating information access and user support. The principal theory for this research study was grounded in the Technology Acceptance Model (TAM), which proposed that the perceived ease of use and perceived usefulness of technology are critical determinants of users' intentions to adopt and use that technology.Findings – The findings revealed that ChatGPT demonstrated an understanding of its function in this context, enhancing the user experience, addressing information queries, and navigating the diverse needs of library patrons. It acknowledged both its contributions and limitations. It recognised challenges in facilitating information access and user support, indicating a nuanced understanding of its role in assisting library patrons.Implications – This research contributes to the field of library and information science and offers insights into the broader implications of incorporating AI in information service settings.Originality – The study is one of the few exploring the perspectives on how it can help improve library services.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.423 Zit.