Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
What's the difference between human‐written manuscripts versus ChatGPT‐generated manuscripts involving “human touch”?
21
Zitationen
2
Autoren
2025
Jahr
Abstract
AIM: To determine whether ChatGPT generates a manuscript with a "human touch" with appropriate inputs, and if yes, what's the difference between human writing versus ChatGPT writing. This is because the presence or absence of human touch may characterize human writing. METHODS: A descriptive study. The first author wrote a Disagreement Letter (Letter 1). Then, disagreement points and "human touch" were provided as input into ChatGPT-4 and tasked with generating a Letter (Letter 2). The authors, seven experienced researchers, and ChatGPT evaluated the readability of Letters 1 and 2. RESULTS: The authors, researchers, and ChatGPT, all reached the same conclusions: the human-written Letter 1 and the ChatGPT-generated Letter 2 had similar readability and similarly involved human touch. Some researchers and ChatGPT recognized slight differences in formal or informal and personal or nonpersonal tones between them, which they considered may not affect paper acceptance. CONCLUSIONS: Human touch is not humans' exclusive possession. The distinction between the human writing versus ChatGPT writing is considered to be present not in the output (manuscript) but in the process of writing, that is, the presence or absence of a joy of writing. Artificial intelligence should aid in enhancing, or at the very least, not impede the human joy. This discussion deserves ongoing exploration.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.700 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.605 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.133 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.873 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.