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AI Education Lab
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A lab of educational technology that supports how people learn through AI and educational data.
AboutAbout the lab
Supporting learning with AI and data.
The AI Education Lab studies systems that support education and learning using ICT and AI.
It works on learning analytics, which analyzes educational big data such as learning logs, and on generating learning materials with generative AI.
The lab places importance on practice in educational settings and aims to scientifically analyze and solve problems in education and learning.
Key terms
- Educational big data
- Large amounts of data collected in the course of learning, such as e-book viewing records
- Learning analytics
- An approach that analyzes learning data and uses it to improve education and learning
- Pattern mining
- An analysis method that finds frequently occurring sequences and trends in large amounts of data
- Clustering
- An analysis that groups students with similar ways of learning based on learning logs
ResearchResearch themes
The lab explores what an intelligent information environment that promotes people's transmission of knowledge, awareness, and creation should be, and studies ICT-based support for education and learning. Based on learners' characteristics and learning logs, it has worked on generating, recommending, and improving learning materials and on analyzing learning behavior.
*Illustrative imageGenerating personalized learning materials
Based on learners' characteristics and learning histories, the lab is building a system that uses generative AI technology to dynamically create and recommend materials suited to each learner.
*Illustrative imageLearning material recommendation tailored to learners
The lab developed a system that uses learners' behavior patterns and VARK learning styles to recommend the optimal materials according to timing and individual needs.
*Illustrative imageImproving learning materials through viewing logs
Applying pattern mining to the viewing logs of self-made materials, the lab extracted improvements that go into the content of the materials, and proposed and verified improvement methods.
*Illustrative imageCluster analysis of educational big data
Based on e-book learning logs, the lab clustered learning behaviors and analyzed the characteristics of each learning style. It confirmed a tendency for students who reread more to learn more efficiently.
MembersFaculty
- ProfessorChengjiu Yin
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Online learning img” Oconnoras / CC BY-SA 4.0 (cropped)
- Generating personalized learning materials: “First Grade Classroom-Egypt-2018 (cropped)” Linda Herrera / CC BY-SA 4.0 (cropped)
- Learning material recommendation tailored to learners: “Sias Library - Students Studying 2017” Gary Todd / CC0 (cropped)
- Improving learning materials through viewing logs: “Italy, E-Learning, Covid19” Nessma Elaassar / CC BY-SA 4.0 (cropped)
- Cluster analysis of educational big data: “Spectral clusterization example” BogdanShevchenko / CC BY-SA 4.0 (cropped)
- Related lab (Human Data Interaction Lab): “School of Data Science - University of Virginia - terrace” School of Data Science at the University of Virginia / CC BY 4.0 (cropped)
- Related lab (Image and Media Understanding Lab): “Moskovskaya” Artyom Svetlov / CC BY-SA 4.0 (cropped)
- Related lab (Computer Vision, Graphics, and VR Lab): “Einsatz von Augmented Reality im Service von HAHN Automation” HAHN Group / CC BY-SA 4.0 (cropped)
- Related lab (Behavior Informatics Lab): “The scope of embodied cognition fr” John J. Madrid / CC BY 4.0 (cropped)
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