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ABRI Seminar Thiemo Wambsganss 16 April 2024 16:00 - 17:00

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We are happy to invite you to the ABRI Seminar Can large language models revolutionize learner-centered education? by Dr. Thiemo Wambsganss (Bern University of Applied Sciences, Switzerland) organized by ABRI and the KIN Center for Digital Innovation.

The seminar will take place on Tuesday, April 16th, from 16:00 to 17:00 (NU-4B43).  

Abstract
In this talk, we will dive into the design and effects of human-centered AI-based learning tools and how large language models impacted the way we design and evaluate educational technologies. Thiemo Wambsganss will share his vision of human-centered AI-based tools and will explain how he designs and tests the effects of adaptive writing assistants for metacognitive skills (such as argumentation, empathy, or mindfulness) or conversational agents (aka chatbots) to better tutor students in their learning experience. Specifically, he will take a deep dive into the research of his system ArgueLearn which helped students develop better argumentation skills over time through the provision of Machine Learning feedback on their logical errors.

About ABRI Seminar Thiemo Wambsganss

Starting date

  • 16 April 2024

Time

  • 16:00 - 17:00

Location

  • NU-4B43
  • NU-4B43

Address

  • De Boelelaan 1111
  • 1081 HV Amsterdam

Organised by

  • ABRI and the KIN Center for Digital Innovation

Language

  • English

Biography Thiemo Wambsganss

Biography Thiemo Wambsganss

Thiemo Wambsganss is a Tenure-Track Research Assistant Professor and director of the Human-Centered AI-based Systems (HAIS) Lab at Bern University of Applied Sciences in Bern Switzerland. His research lies in the general area of Human-Computer Interaction with influences from Natural Language Processing, and Machine Learning. He strives to understand how humans perceive, interact, and learn with intelligent tools. Based on these insights, he builds adaptive user interfaces that go beyond static and rule-based interaction to advance the capabilities when working and learning in the digital world. Specifically, he is driven by the vast opportunities to enhance and improve pedagogical scenarios based on recent advantages in Natural Language Processing and Machine Learning to enable humans to learn self-reliant and individually independent of an educator or their background. To do so, he uses techniques from Artificial Intelligence (AI) to build AI-powered education tools. His research follows three connected main lines of work: 1) creating and studying pedagogical conversational agents or intelligent writing support systems to improve educational scenarios, 2) exploring methods and techniques to model student performance in textual data, and 3) building computational approaches that control when, where and how to provide students intelligent feedback and self-evaluation. More information through: https://thiemowa.github.io/