IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-031-88052-0_14.html
   My bibliography  Save this book chapter

AI and the Evolution of Higher Education: A Strategic Approach

In: Digital Management and Artificial Intelligence

Author

Listed:
  • Vasil Kikutadze

    (East European University)

  • Tamta Lekishvili

    (East European University)

Abstract

In the advent of artificial intelligence (AI) is profoundly transforming the landscape of higher education, compelling institutions to re-evaluate and adapt their learning outcomes to prepare students for an AI-driven future. This paper explores the strategic approaches necessary for aligning educational program learning outcomes with the evolving demands imposed by AI technologies. We analyze the multifaceted impacts of AI on various academic disciplines, examining both the opportunities for enhanced learning and the potential challenges that institutions face in integrating AI into curricula. Central to this discussion is the need for a paradigm shift in educational objectives to cultivate skills that are complementary to AI, such as critical thinking, ethical reasoning, and advanced problem-solving. The paper advocates for a comprehensive framework that encompasses curriculum redesign, faculty development, and the integration of AI literacy across higher education. By analyzing best practices and insights from higher education experts, we identify crucial strategies for institutions to effectively revise their learning outcomes, thereby better preparing students with the essential skills needed to excel in an AI-driven environment.The paper concludes with a set of actionable recommendations for policymakers, educators, and academic leaders to foster an educational ecosystem that is responsive to the rapid technological changes brought about by AI.

Suggested Citation

  • Vasil Kikutadze & Tamta Lekishvili, 2025. "AI and the Evolution of Higher Education: A Strategic Approach," Springer Proceedings in Business and Economics, in: Richard C. Geibel & Shalva Machavariani (ed.), Digital Management and Artificial Intelligence, pages 179-195, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-88052-0_14
    DOI: 10.1007/978-3-031-88052-0_14
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-031-88052-0_14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.