Artificial intelligence is rapidly reshaping higher education, yet many universities lack practical frameworks for integrating AI into leadership and administrative decision-making. This interactive workshop explores how institutions can move from experimentation to strategic implementation of AI across academic operations, student advising, and international collaboration.

Designed for university leaders, administrators, and academic staff, the session presents concrete use cases of AI-supported workflows in admissions, program coordination, advising, and institutional planning. Participants will engage with practical tools and structured activities that demonstrate how generative AI can enhance efficiency, support data-informed decisions, and strengthen global collaboration while maintaining ethical and responsible practices.

The workshop situates AI adoption within broader institutional transformation, emphasizing leadership capacity, governance, and change management. Participants will examine how digital innovation can align with institutional values, equity considerations, and long-term strategy. Through guided exercises, attendees will identify high-impact implementation areas within their own institutions and outline realistic next steps for integrating AI into daily operations.

By combining strategic perspective with hands-on exploration, the session aims to bridge the gap between emerging technologies and practical application in higher education leadership. It is suitable for both institutions beginning to explore AI and those seeking more structured approaches to implementation.

Authors:
Rubina Danilova, American University of Armenia, Armenia

About the Presenter(s)
Dr.Danilova is Operations Manager and Lecturer at the American University of Armenia and an independent trainer in AI for higher education. She develops practical AI workflows for university administration, advising, and international collaboration.

Connect on Linkedin
https://www.linkedin.com/in/rubina-danilova/

See this presentation on the full schedule – On Demand Schedule

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