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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

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Katsamakas, Evangelos, Pavlov, Oleg V., Saklad, Ryan·2024·Sustainability
DOI 10.3390/su16146118
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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.

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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.

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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

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