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Design Assignments and Create Rubrics with AI
Registration
Registration is now closed (this event already took place).
Details
Through this workshop participants will learn how AI can streamline the creation of assignments, improve the alignment and fairness of rubrics. By the end of this workshop, participants will walk away with practical insights and immediate actions they can implement in their courses to enhance student learning outcomes and teaching efficiency.
Course Goals:
1. Quickly Understand AI's Role in Education: Gain a basic understanding of AI’s potential in transforming educational tasks, with a focus on immediate application.
2. Create AI-Enhanced Assignments: Learn simple yet effective techniques to design assignments that leverage AI to meet diverse student needs.
3. Refine Rubrics Using AI: Explore quick methods to use AI for refining rubrics, ensuring they are clear, fair, and aligned with learning outcomes. Outcome:
In this workshop, participants will have gained practical knowledge and hands-on experience with AI tools that can be immediately applied to enhance their teaching practices. They will leave with a clear action plan and the confidence to start integrating AI into their assignments and rubrics processes.
Speakers
Dae-Jin Lee
Dae-Jin Lee obtained his Ph.D. in Mathematical Engineering (area of Statistics) from University Carlos III de Madrid in 2010. Since 2023, he is Assistant Professor at IE University School of Science and Technology where he teaches subjects related to Data Analysis, Statistical Modeling and Computing. Previously, from 2014 to 2022, he was a researcher and research line leader of the Applied Statistics group at the Basque Centre for Applied Mathematics, Centre of Excellence “Severo Ochoa”, where he also coordinated the Knowledge Transfer Unit in the area of Data Science and Artificial Intelligence. Dae-Jin’s research interests include statistical modeling for complex data with applications in several research fields. In particular, he is an expert in smoothing techniques based on penalized splines regression models, tensor product smooths in mixed models framework with applications in biostatistics, biomedicine, epidemiology, environmental problems, or sports sciences. He has more than 10 years of experience in research in multidisciplinary research environments collaborating with economists, engineers, medical doctors, sports scientists, biologists and mathematicians. He has also led research projects funded through public calls and industrial R&D&I projects. His research has been published in scientific journals such as Biometrics, Statistics in Medicine, Statistical Methods in Medical Research, Statistics and Computing, Journal of the Royal Statistical Society (series C – Applied Statistics) or Statistical Modelling among others and his work has been presented in several international conferences. His research network includes collaborations in several countries such as Spain, France, Germany, UK, the Netherlands, Belgium, Chile, South Africa, Australia and the US. Dae-Jin is also involved in scientific organizations and societies, as member of the board of the Spanish Biostatistics Society (Spanish region of the International Biometrics Society) and the Statistical Modelling Society.