HELMeTO 2022

4th International Conference on Higher Education Learning Methodologies and Technologies Online

September 21-23, 2022
Palermo, Italy

Artificial Intelligence and Multimodal Technologies in Education (AI&MTEd ‘22)

Aim and Scope

Artificial Intelligence in Education (AIED) has been a research topic for several years, and it recently gained a boost due to the availability of applicable AI technology on a large scale. In general, AIED deals with the question of 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’s knowledge by using a variety of techniques such as Constraint-Based Modelling, Knowledge Tracing, Computer-assisted Instruction, Intelligent Tutoring Systems, and Automatic Grading Systems.

The majority of the existing systems focus 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) have become possible. In this context, 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 of artificial intelligence and multimodal technologies in education comprise of:
– which sensor combinations deliver meaningful data on human performance?
– what is the best way to represent real-time data so that they can 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?

Potential Scopes of Interest

– Natural human-computer interaction
– Immersive learning scenarios
– Complex skills learning
– Authentic practice through multimodal technologies
– Sensor-based learning
– Wearable-enhanced learning
– Assessment methodologies for “in the wild” studies
– Multimodal interfaces for learning
– Motion capture
– Affective computing
– Context-aware learning technologies
– Artificial intelligence for automated feedback
– Expert-based personalised training & learning


Daniele Di Mitri, Leibniz Institute for Research and Information in Education,
Jan Schneider, Leibniz Institute for Research and Information in Education,
Bibeg Limbu, Leibniz Institute for Research and Information in Education,
Pietro Picerno, E-Campus

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