Empowering Higher Education with Generative AI: A Hands-On Workshop for Teaching Students How to Use Large Language Models

Abstract

In the rapidly evolving landscape of higher education, integrating generative artificial intelligence has become essential for preparing students how to learn and work in this new world where everyone will have access to AI assistants and AI co-pilots. This workshop will teach faculty members a structured approach to instruct students on using large language models effectively. The session will focus on four key skills that are valuable across disciplines. 1) Condensing Information: techniques for summarizing large volumes of text using AI tools, ensuring clarity and conciseness. 2) Desk Research: utilizing AI for efficient and comprehensive research, gathering and synthesizing information from diverse sources. 3) Creative Thinking: leveraging AI to foster creativity, generate new ideas, and explore innovative solutions across various fields. 4) Content Generation: practical applications of generative AI for producing high-quality content. Participants will explore strategies for integrating these skills into their curriculum, leveraging AI tools like ChatGPT and Claude for productive academic purposes. Through interactive demonstrations and guided practice, educators will gain practical skills in implementing generative AI applications in their teaching and assignments. Participants will leave with actionable insights to contribute to the broader academic conversation on AI in education.

Presenters

Peruta Adam
Program Director and Associate Professor, Advanced Media Management, Syracuse University, New York, United States

Nicholas Bowman
Associate Professor, S.I. Newhouse School of Public Communications, Syracuse University, New York, United States

Details

Presentation Type

Workshop Presentation

Theme

2025 Special Focus—Learning from Artificial Intelligence: Pedagogical Futures and Transformative Possibilities

KEYWORDS

AI in Education, Generative AI, Pedagogical Strategies, Large Language Models