Bringing Generative Artificial Intelligence (GenAI) to Education
Generative Artificial Intelligence (GenAI) has rapidly emerged as a transformative technology across multiple research communities. With its exceptional capabilities in generating images, audio, video, and natural language, GenAI has achieved unprecedented success in various domains. In this tutorial, we aim to provide a comprehensive overview of integrating GenAI into education, presenting recent advancements in the field, exploring both its opportunities and challenges, and inspiring further research in this direction. We begin by introducing fundamental concepts of GenAI to equip attendees with the necessary background knowledge for the discussions that follow. Next, we examine the impact of GenAI on education, summarizing key transformations, opportunities, and challenges to provide a broad perspective on its applications in educational settings. Building on this foundation, we present detailed case studies of representative works that implement GenAI in education. By showcasing pioneering research and applications, we illustrate the benefits, challenges, and limitations of GenAI-driven educational tools. Additionally, we have prepared a live demonstration session where participants will experience two LLM-powered educational systems: an interactive learning platform and an automated grading tool. Through hands-on interaction, attendees will gain firsthand insight into how GenAI is shaping education. Finally, we conclude with a discussion session, encouraging participants to share their thoughts, insights, and ideas on the future of GenAI in education. By the end of this tutorial, we hope attendees will gain a solid understanding of current trends in GenAI-driven education, develop deeper insights into this evolving field, and find inspiration for future research and applications.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-07-12
- DOI
- https://doi.org/10.5281/zenodo.15870319
- Primary Topic
- Cancer, Hypoxia, and Metabolism
- Type
- article
- Field-Weighted Citation Impact
- 0.00