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Assembly Series

A tradition of convening thought leaders since 1953

McKelvey School of Engineering

Brown School

Build Your Own AI Tutor with GPTs Workshop

Tuesday, February 27 | 12:00 PM - 1:00 PM

Virtual Event

Are you fascinated by the power of AI in education? Join us for an exclusive workshop, where you’ll learn to harness the capabilities of customized ChatGPT and its vector database to create a personal AI tutor. This workshop is perfect for undergraduate and graduate students eager to explore the forefront of educational technology.

Why Attend?

  • Personalized Learning 24/7: Discover how to program an AI tutor that offers round-the-clock assistance, adapting to your unique learning need and style.
  • Interactive Tools: Learn to integrate quizzes, exercises, and conceptual summaries, enhancing the learning experience.
  • Expert Insights: Gain insights from Professor An, a pioneer in utilizing ChatGPT for research productivity. You can use his expertise and learn from his latest book.
  • Hands-On Experience: Engage in practical sessions where you’ll apply your knowledge to build an AI tutor tailored to facilitate your learning.
  • Future of Education: Be at the forefront of educational innovation, understanding the role of AI in shaping future learning environments.

Whether aiming to boost your learning process or explore new teaching methodologies, this workshop is your gateway to the future of education.

Register to attend this workshop.

Topic

Science & Technology

Website

https://aicademe.publish.library.wust...

Hashtag

#AI Workshop Series

Event Contact

Ruopeng An, ruopeng@wustl.edu; Yuyi Yang, y.yuyi@wustl.edu

Speaker Information

Ruopeng An, Associate Professor, Brown School

Ruopeng An conducts research to assess population-level policies, local food and built environment, and socioeconomic determinants that affect individuals’ dietary behavior, physical activity, sedentary lifestyle, and adiposity in children, adults of all ages, and people with disabilities. His research aims to develop a well-rounded knowledge base and policy recommendations that can inform decision-making and the allocation of resources to combat obesity.

An’s research has been funded by federal agencies and public/private organizations (e.g., OpenAI, Abbott, Amgen). He has wide teaching and methodological expertise, including applied artificial intelligence (machine and deep learning), quantitative policy analysis (causal inference, cost-benefit and cost-effectiveness analysis, and microsimulation), applied econometrics and regression analysis, and systematic review and meta-analysis. He founded and chairs the Artificial Intelligence and Big Data Analytics for Public Health (AIBDA) Certificate program and hosts the “Artificial Intelligence in the Social Sciences” Open Classroom series. He has repeatedly been recognized for teaching excellence, receiving student evaluations in the top 10% of University faculty.

With over 200 peer-reviewed journal publications, Dr. An is recognized as one of Elsevier’s top 2% most cited scientists. His work has been highlighted by media outlets such as TIME, New York Times, Los Angeles Times, Washington Post, Reuters, USA Today, Bloomberg, Forbes, Atlantic, Guardian, FOX, NPR, and CNN. He serves on research grants and expert panels for NIH, CDC, NSF, HHS, USDA, and the French National Research Agency. He is a Fellow of the American College of Epidemiology.

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