ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing

By Brady D. Lund, Ting Wang, Nishith Reddy Mannuru, Bing Nie, Somipam R. Shimray, Ziang Wang. J. Assoc. Inf. Sci. Technol., 2023

785 citations recorded by the source corpus.

This article discusses OpenAI's ChatGPT, a generative pre‐trained transformer, which uses natural language processing to fulfill text‐based user requests (i.e., a “chatbot”). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT‐3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/da9683e826c37a6383c124b5c6cddefcb35ee8fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Thippa Reddy Gadekallu
2. Gero Strobel
3. Gokul Yenduri
4. Leonardo Banh
5. Praveen Kumar Reddy Maddikunta

In Health Informatics

1. Gero Strobel
2. Leonardo Banh

In Radiology, Nuclear Medicine and Imaging

1. Thippa Reddy Gadekallu
2. Gero Strobel
3. Leonardo Banh
4. Praveen Kumar Reddy Maddikunta

Citations in the corpus

• GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions (2023, background): “Another challenge that applies to all learning technologies is that data bias is possible in GPTs [138].”
• Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA (2023, neutral): “Indeed, previous studies have revealed that under specific circumstances, ChatGPT may sometimes generate inaccurate output such as references, citations, mathematical expressions, or scientific conclusions (e.g., Lund et al., 2023; Sallam, 2023).”
• Generative artificial intelligence (2023, background): “…and automating even traditionally creative tasks, e.g., by generating customized contextual texts or images, facilitating new opportunities for businesses to innovate and differentiate themselves in the competitive economic landscape (Dwivedi et al., 2021; Lund et al., 2023; Pavlik, 2023).”
• The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions (2023, neutral): “135 Organizations may find themselves in a minefield, whereby they are automatically at a disadvantage compared to their rivals if they do not utilize such platforms.”
• Mobile Game Evaluation Method Based on Data Mining of Affective Time Series (2025, unknown): “Below, we describe the founding concepts of the method, which were jointly developed by the research team with the experts from the gaming company we worked with.”