ChatGPT and large language models in academia: opportunities and challenges
By Jesse G. Meyer, Ryan J. Urbanowicz, Patrick C. N. Martin, Karen O'Connor, Ruowang Li, Pei-Chen Peng, T. Bright, Nicholas P. Tatonetti, K. Won, Graciela Gonzalez-Hernandez and 1 more. BioData Mining, 2023
514 citations recorded by the source corpus.
The introduction of large language models (LLMs) that allow iterative “chat” in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic’s perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.
Subfields: Artificial Intelligence, Health Informatics
View Paper: https://www.semanticscholar.org/paper/e95b10b9a8415874f24275b665628fc340eee5dc
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Citations in the corpus
- Impact of AI assistance on student agency (2023, background): “…educators in various aspects of pedagogy, enhancing productivity, generating detailed feedback, and reshaping the peer review process while also raising noteworthy considerations related to ethics, biases, and transparency (Cooper, 2023; Donker, 2023; Li, Patel, & Du, 2023; Meyer et al., 2023).”
- Enhancing patient care with AI agents: integrating advanced chatbot technologies for improved healthcare delivery (2026, unknown): “Data privacy, ethical considerations, and regulatory compliance also present significant barriers to the widespread adoption of these technologies in healthcare settings Meyer et al. [40], Milano et al. [41], Raza et al. [42], Zhou et al. [43, 44], Zeng et al. [45].”