Welcome to the Era of ChatGPT et al.: The Prospects of Large Language Models
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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.
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Assessment and evaluation emerged as the primary pattern, with faculty using GenAI to generate NCLEX-style questions and provide automated student feedback.
“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
The section “Most of it is yet to come” discusses how LLMs may affect the design and evaluation of assignments, not an observed primary pattern of faculty use. Searches for NCLEX-style questions and automated student feedback found no passage addressing those uses or identifying assessment and evaluation as the primary pattern. (LLM, conf=0.76)
Experimental — accuracy not yet measuredExperts 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.
“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)
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Bohara, D. K., & Rana, K. (2024) · SN Social Sciences · doi:10.1007/s43545-024-01003-7 · Cited by 18 · see citing works
Machine learning in biological research: key algorithms, applications, and future directions
Alam, M. N. U., Basava, K., Chitransh, A., Fattah, H. M. A., Garcia‐Verdugo, H. D., Lo, S.-H., Lohchab, T., Martinet, K. M., Román‐Palacios, C., Salazar, J. C., et al. (2025) · BMC Biology · doi:10.1186/s12915-025-02424-3 · Cited by 23 · see citing works
Volk, S. C., Schäfer, M. S., Lombardi, D., Mahl, D., & Yan, X. (2024) · Public Understanding of Science · doi:10.1177/09636625241268910 · Cited by 18 · see citing works
Lewandowski, T., Kučević, E., Leible, S., Poser, M., & Böhmann, T. (2023) · Electronic Markets · doi:10.1007/s12525-023-00662-3 · Cited by 17 · see citing works
Li, J., Yang, Y., Mao, C., Pang, P. C., Zhu, Q., Xu, D., & Wang, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/66344 · Cited by 17 · see citing works
Amankwaa, I., Ekpor, E., Cudjoe, D., Kobiah, E., Fuseini, A.-K. J., Diebieri, M., Gyamfi, S., & Brownie, S. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106822 · Cited by 20 · see citing works
On Early Detection of Hallucinations in Factual Question Answering
Snyder, B., Moisescu, M., & Zafar, M. B. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671796 · Cited by 16 · see citing works
Chiesa-Estomba, C. M., Speth, M. M., Mayo‐Yáñez, M., Liu, D. T., Maniaci, A., & Borsetto, D. (2023) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-023-08382-2 · Cited by 16 · see citing works
ChatGPT in Academic Writing and Publishing: An Overview of Ethical Issues
Bakla, A. (2023) · Advances in Educational Technologies and Instructional Design · doi:10.4018/978-1-6684-9893-4.ch005 · Cited by 15 · see citing works
Ng, J. Y. (2025) · Integrative Medicine Research · doi:10.1016/j.imr.2025.101222 · Cited by 18 · see citing works
Seok, J., Lee, B. H., Kim, D., Bak, S., Kim, S., Kim, S.-T., & Park, S.-M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2443263 · Cited by 15 · see citing works
Canboy, B., & Khlif, W. (2025) · International Journal of Production Economics · doi:10.1016/j.ijpe.2025.109550 · Cited by 15 · see citing works
Slow Writing with ChatGPT: Turning the Hype into a Right Way Forward
Sirisathitkul, C. (2023) · Postdigital Science and Education · doi:10.1007/s42438-023-00441-5 · Cited by 14 · see citing works
Evolving Generative AI: Entangling the Accountability Relationship
Elliott, M. T. J., Deepak, P., & MacCárthaigh, M. (2024) · Digital Government Research and Practice · doi:10.1145/3664823 · Cited by 17 · see citing works
Environment and sustainability development: A ChatGPT perspective
Bhaskar, P., & Seth, N. (2024) · CRC Press eBooks · doi:10.1201/9781003471059-8 · Cited by 14 · see citing works
Evaluating AI-generated responses from different chatbots to soil science-related questions
Khanifar, J. (2025) · Soil Advances · doi:10.1016/j.soilad.2025.100034 · Cited by 14 · see citing works
The ChatGPT application on quality management: a comprehensive review
Atalan, A. (2025) · Journal of Management Analytics · doi:10.1080/23270012.2025.2484225 · Cited by 14 · see citing works
- 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.
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