The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Welcome to the Era of ChatGPT et al.: The Prospects of Large Language Models
Open accessAbstract
The emergence of Large Language Models (LLMs) in combination with easy-to-use interfaces such as ChatGPT, Bing Chat, and Google’s Bard represent both a Herculean task and a sublime opportunity for Business and Information Systems Engineering . The technology and its applications already have considerable impact in many domains directly related to the design, operation, and application of information systems. In this editorial, we seek to explore this new reality as researchers, practitioners, and legislators will – in some form or another – have to deal with it. Our goal is to provide insights and encourage research in this new, exciting, and rapidly developing field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Assessment and evaluation emerged as the primary pattern, with faculty using GenAI to generate NCLEX-style questions and provide automated student feedback”
From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Teubner et al., 2023
Experimental — accuracy not yet measuredIn the citing paper
“In line with scholars such as Teubner et al. (2023), experts in our study voiced concerns that ChatGPT and other LLMs have the potential to widen the digital divide between researchers who possess technical know-how and researchers who do not.”
From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Teubner et al. (2023)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437
Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01618-2 · Cited by 1,078
ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
(2023) · Nature · doi:10.1038/d41586-023-00191-1 · Cited by 605
ChatGPT Goes to Law SchoolPreprint
Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4335905 · Cited by 565
Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?
Horton, J., Filippas, A., & Manning, B. (2023) · National Bureau of Economic Research · doi:10.3386/w31122 · Cited by 329
Bommarito, M. J., & Katz, D. M. (2022) · doi:10.2139/ssrn.4314839 · Cited by 181
Copy, transform, combine: Exploring the remix as a form of innovation
Flath, C. M., Friesike, S., Wirth, M., & Thiesse, F. (2017) · Journal of Information Technology · doi:10.1057/s41265-017-0043-9 · Cited by 96
Self-citationBig Digital Platforms: Growth, Impact, and Challenges
van der Aalst, W., Hinz, O., & Weinhardt, C. (2019) · Business & Information Systems Engineering · doi:10.1007/s12599-019-00618-y · Cited by 61
Self-citationImproving Multimodal Interactive Agents with Reinforcement Learning from Human FeedbackPreprint
Abramson, J., Ahuja, A., Carnevale, F., Georgiev, P., Goldin, A., Hung, A., Landon, J., Lhotka, J., Lillicrap, T., Muldal, A., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.11602 · Cited by 48
Research works printed but not linked: 7
- Bommasani R, et al (2021) On the opportunities and risks of foundation models. working paper. https://arxiv.org/abs/2108.07258 · arXiv
- Brown TB, et al (2020) Language models are few-shot learners. In: NeurIPS 2020 Proceedings, pp 1–25
- Chang M, et al (2023) In2Writing: the second workshop on intelligent and interactive writing assistants. https://in2writing.glitch.me/. Accessed 10 Feb 2023
- Kosinski M (2023) Theory of mind may have spontaneously emerged in large language models. working paper. https://arxiv.org/abs/2302.02083 · arXiv
- Kung TH, et al (2022) Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. working paper. https://www.medrxiv.org/content/https://doi.org/10.1101/2022.12.19.22283643v2 · DOI
- Luo R, et al (2023) BioGPT: generative pre-trained transformer for biomedical text generation and mining. working paper. https://arxiv.org/pdf/2210.10341.pdf · arXiv
- Ziemann M, Eren Y, El-Osta A (2016) Gene name errors are widespread in the scientific literature. Genome Biol 17(177):1–3
Cited by
Sorted by citations
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 2,267
Generative artificial intelligence
Banh, L., & Strobel, G. (2023) · Electronic Markets · doi:10.1007/s12525-023-00680-1 · Cited by 879
Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., Paliszkiewicz, J., & Ziemba, E. (2023) · Entrepreneurial Business and Economics Review · doi:10.15678/eber.2023.110201 · Cited by 584
“Finally, the unregulated AI market might lead to legal ambiguities related to ownership, privacy, and intellectual property rights (Teubner et al. , 2023).”
BackgroundStrzelecki, A. (2024) · Innovative Higher Education · doi:10.1007/s10755-023-09686-1 · Cited by 477
“ChatGPT in university education presents challenges to the academic community wherever ChatGPT is available (Teubner et al., 2023).”
Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S. L., Dron, J., Stracke, C. M., Singh, L., Crompton, H., et al. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.777 · Cited by 320
Metadata
- Authors
- Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O.
- Affiliations
- Einstein Center Digital Future (DE)
- University of Würzburg (DE)
- Karlsruhe Institute of Technology (DE)
- Goethe University Frankfurt (DE)
- Corresponding author
- Teubner, Timm
- Venue
- Business & Information Systems Engineering
- Published
- 2023-03-13
- Volume
- 65
- Issue
- 2
- Pages
- 95-101
- ISSN
- 1867-0202 (ISSN-L)
- 2363-7005
- OpenAlex
- W4360980513
- Cited by
- 384
- References
- 11
- Citations by year
- 2026: 78 · 2025: 135 · 2024: 117 · 2023: 54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Business & Information Systems Engineering (publishedVersion, cc-by)
- RWTH Publications (RWTH Aachen) (publishedVersion)
- Econstor (Econstor) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
- Funders
- Technische Universität Berlin
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, ChatGPT, Bing Chat, Google Bard, information systems, business applications of AI, AI regulation, research opportunities
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 ›
Are you an author, or do you see a problem with this page? Request a review
