Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI‐assisted technologies in scientific research and education
Open accessAbstract
The emergence of large language models (LLMs) and assisted artificial intelligence (AI) technologies have revolutionized the way in which we interact with technology. A recent symposium at the Walter and Eliza Hall Institute explored the current practical applications of LLMs in medical research and canvassed the emerging ethical, legal and social implications for the use of AI-assisted technologies in the sciences. This paper provides an overview of the symposium's key themes and discussions delivered by diverse speakers, including early career researchers, group leaders, educators and policy-makers highlighting the opportunities and challenges that lie ahead for scientific researchers and educators as we continue to explore the potential of this cutting-edge and emerging technology.
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Other studies suggested that generative AI systems such as ChatGPT are increasingly being adopted to assist researchers with literature synthesis, information organization, and preliminary drafting.
“Likewise, other studies suggested that generative AI systems such as ChatGPT are increasingly being adopted to assist researchers with literature synthesis, information organization, and preliminary drafting, reflecting a growing transition toward AI-assisted scholarly workflows [12, 40].”
From Developing the HAKI model as a conceptual framework for AI-assisted academic research (2026) · cites this paper as [12]
The Abstract (page unavailable) reports growing adoption of AI tools by researchers. “Use of ChatGPT to assist with writing and reading grants and papers” (page unavailable) describes summarizing scientific writing and assisting with writing, which supports much of the paraphrase. The copy does not clearly establish increasing adoption specifically for information organization or preliminary scholarly drafting, and it cautions that generated summaries may be inaccurate. (LLM, conf=0.84)
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Giotopoulos, K. C., Papadopoulos, D., & Halkiopoulos, C. (2026) · Springer proceedings in business and economics · doi:10.1007/978-3-032-17545-8_15
Owusu, S. O., Gil, M. T., Tutu-Boahene, B., & Brew, Y. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01997-4
- Authors
- Borger, J. G., Ng, A. P., Anderton, H., Ashdown, G. W., Auld, M., Blewitt, M. E., Brown, D. V., Call, M. J., Collins, P., Freytag, S., Harrison, L. C., Hesping, E., Hoysted, J., Johnston, A., McInneny, A., Tang, P., Whitehead, L., Jex, A., & Naik, S. H.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Borger, Jessica G
- Naik, Shalin H
- ORCID
- Venue
- Immunology & Cell Biology
- Publisher
- Wiley
- Published
- 2023-09-18
- Volume
- 101
- Issue
- 10
- Pages
- 923-935
- ISSN
- 0004-945X (ISSN-L)
- 0818-9641
- 1440-1711
- OpenAlex
- W4386826322
- Cited by
- 103
- References
- 7
- Citations by year
- 2026: 26 · 2025: 44 · 2024: 32 · 2023: 1
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Immunology and Cell Biology (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- large language models, ChatGPT, medical research, scientific research, science education, AI ethics, legal implications of AI, societal impact of AI, early-career researchers, AI policy
- MeSH terms
- Artificial Intelligence, Technology, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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