CoChat
Sign Up
Cited by 291Artificial Intelligence in EducationDigital Transformation of Higher EducationHigher Education Governance and Development

The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.

Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach

Open access
Katsamakas, Evangelos, Pavlov, Oleg V., Saklad, Ryan·2024·Sustainability
DOI 10.3390/su16146118
Open this paper in CoChat — free account

Abstract

Artificial intelligence (AI) advances and the rapid adoption of generative AI tools, like ChatGPT, present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systems approach that captures a holistic view of the structure and dynamics of the AI transformation of higher education institutions (HEIs). To fill this gap, this article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. We identify important variables and their relationships and map multiple reinforcing and balancing feedback loops accounting for the forces that drive the AI transformation and its impact on value creation in a typical HEI. The model shows how, motivated by AI technology advances, the HEI can invest in AI to improve student learning, research, and administration while dealing with academic integrity problems and adapting to job market changes by emphasizing AI-complementary student skills. We explore model insights, scenarios, and policy interventions and recommend that HEI leaders become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding policy traps that may lead to decline. We also discuss the notion of HEIs influencing the direction of AI and directions for future research on AI transformation and the sustainability of HEIs.

Overview

Integrity signals

Registry

Crossref · 10.3390/su16146118

Cites

107 of 107 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Show all 106 references

Research works printed but not linked: 11

  1. Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin McGraw-Hill.
  2. Katsamakas, 2020, AI and Business Model Innovation: Leverage the AI Feedback Loops, J. Bus. Model., 8, 22
  3. Russell, S., and Norvig, P. (2022). Artificial Intelligence: A Modern Approach, Prentice Hall. [4th ed.].
  4. Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., and Dong, Z. (2023). A Survey of Large Language Models. arXiv.
  5. Chen, 2022, Two Decades of Artificial Intelligence in Education, Educ. Technol. Soc., 25, 28
  6. Hassabis, D. (2022). Using AI to Accelerate Scientific Discovery, Oxford University. Institute for Ethics in AI.
  7. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., and Sun, L. (2023). A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. arXiv.
  8. Li, P., Yang, J., Islam, M.A., and Ren, S. (2023). Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models. arXiv.
  9. Peng, S., Kalliamvakou, E., Cihon, P., and Demirer, M. (2023). The Impact of AI on Developer Productivity: Evidence from GitHub Copilot. arXiv.
  10. Burns, J.R., and Musa, P. (2001, January 23–27). Structural Validation of Causal Loop Diagrams. Proceedings of the 19th International Conference of the System Dynamics Society, Atlanta, GA, USA.
  11. Tu, X., Zou, J., Su, W.J., and Zhang, L. (2023). What Should Data Science Education Do with Large Language Models?. arXiv.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Katsamakas, E., Pavlov, O. V., & Saklad, R.
Affiliations
  • Fordham University (US)
  • Worcester Polytechnic Institute (US)
Corresponding author
Katsamakas, Evangelos
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2024-07-17
Volume
16
Issue
14
Pages
6118
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su16146118
OpenAlex
W4400722535
Cited by
195
References
106
Citations by year
2026: 116 · 2025: 71 · 2024: 7
FWCI
72.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Education › Digital Transformation of Higher Education
  • Social Sciences › Education › Higher Education Governance and Development
Keywords
artificial intelligence in higher education, generative AI, ChatGPT, higher education institutions, AI transformation, causal loop diagrams, feedback loops, systems thinking, academic integrity, AI policy, sustainability of higher education, value creation

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