Navigating AI Detection Tools for Academic Integrity
In an era where AI writing tools are both a boon and a bane for academic integrity, the ability to discern AI-generated content has become indispensable. Professor Ruopeng An will be leading an exclusive workshop designed for WashU professors and instructors, aiming to demystify the landscape of AI detection technologies.
Why Attend?
– Comprehensive Overview: Survey the most popular AI detection tools currently shaping the educational landscape.
– Deep Dive into Technology: Understand the underlying algorithms and key approaches that power these tools, equipping you with the knowledge to critically assess their effectiveness.
– Practical Insights: Explore the caveats of using AI detection technologies. We will provide practical suggestions to navigate the challenges posed by these tools, ensuring you can make informed decisions in your academic endeavors.
– Futuristic Outlook: Engage in thought-provoking discussions on the future of AI in education. Deliberate the ethical considerations and potential implications of using (or not using) AI detection tools in maintaining academic integrity.
Join us for an enlightening session that promises to equip you with the insights and strategies necessary to navigate the complex interplay between AI and academic integrity. Whether you’re a skeptic of AI detection tools or an enthusiast eager to learn about their potential, this workshop will provide you with a balanced perspective on the current state and future possibilities.
Professor 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.
Register to Attend