From Artificial Intelligence in Education to Multimodal Learning Experience

Thematic Workshop Artificial Intelligence in Education (AIED) is a research topic for several years, which gained a recent boost due to the availability of applicable AI technology at large scale. In general, AIED deals with the question on how to use approaches and technologies of AI to better support learners

Speakers

Daniele Di Mitri
DIPF, Germany
Roland Klemke
Open University of The Netherlands, The Netherlands / TH Köln, Germany
Bibeg Limbu
Leiden Delft Erasmus Center for education and learning
neutral portrait picture
Khaleel Asyraaf
Open University of The Netherlands
Jan Schneider
DIPF, Germany

Start

25/05/2020 - 14:00

End

25/05/2020 - 17:30

Thematic Workshop

Artificial Intelligence in Education (AIED) is a research topic for several years, which gained a recent boost due to the availability of applicable AI technology at large scale. In general, AIED deals with the question on how to use approaches and technologies of AI to better support learners with individual guidance, feedback, and tailored processes. The basis for these approaches are methods to represent the student knowledge, using a variety of techniques such as Constraint-Based Modelling, Knowledge Tracing, Computer-assisted instruction, Intelligent Tutoring Systems, or Automatic Grading Systems.

The majority of existing systems focuses on content-related subjects such as mathematics, statistics and physics, today enhanced also with general-purpose tools like chatbots and conversational agents, podcasts, or recommender systems. With the advent of sensor-based multimodal systems as well as augmented and virtual reality, multimodal learning experiences (MLX) become possible, where AI can also support learning and training in relation to psychomotor skills taking also affective and physiological aspects such as stress or concentration levels into account.
Key challenges for AI in MLX comprise:

  • Which sensor combinations deliver meaningful data on human performance?
  • How shall real-time data best be represented to be interpretable by AI systems?
  • How can data between learners or between expert and learner be compared to detect and classify mistakes?
  • How can meaningful guidance and feedback be generated on which modalities to provide the best possible learning experience?

Given the wideness of this field, we propose this workshop to be divided into two parts of 90 minutes each:

  1. The first part is a lecture-style presentation which introduces the rationale of AI in Education and its transformation into MLX.
  2. The second part is a group-work workshop in which each group of participants is given a specific use case.
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