Artificial intelligence and the transformation of higher education
institutions
- URL: http://arxiv.org/abs/2402.08143v1
- Date: Tue, 13 Feb 2024 00:36:10 GMT
- Title: Artificial intelligence and the transformation of higher education
institutions
- Authors: Evangelos Katsamakas, Oleg V. Pavlov, and Ryan Saklad
- Abstract summary: This article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI.
Our model accounts for the forces that drive the AI transformation and the consequences of the AI transformation on value creation in a typical HEI.
The article identifies and analyzes several reinforcing and balancing feedback loops, showing how the HEI invests in AI to improve student learning, research, and administration.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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 systemic approach that captures a holistic view of the AI
transformation of higher education institutions (HEIs). To fill this gap, this
article, taking a complex systems approach, develops a causal loop diagram
(CLD) to map the causal feedback mechanisms of AI transformation in a typical
HEI. Our model accounts for the forces that drive the AI transformation and the
consequences of the AI transformation on value creation in a typical HEI. The
article identifies and analyzes several reinforcing and balancing feedback
loops, showing how, motivated by AI technology advances, the HEI invests in AI
to improve student learning, research, and administration. The HEI must take
measures to deal with academic integrity problems and adapt to changes in
available jobs due to AI, emphasizing AI-complementary skills for its students.
However, HEIs face a competitive threat and several policy traps that may lead
to decline. HEI leaders need to become systems thinkers to manage the
complexity of the AI transformation and benefit from the AI feedback loops
while avoiding the associated pitfalls. We also discuss long-term scenarios,
the notion of HEIs influencing the direction of AI, and directions for future
research on AI transformation.
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