About this Research Topic
The artificial intelligence (AI) based technique has become one of the most popular and effective approaches in scientific studies on brain cognition and digital education (BCDE). In recent years, many studies have been devoted to developing various data-driven AI models to bridge the two fields, i.e., explaining the education psychology from brain function or investigating the education impact on brain development.
In this digital era, modern devices provide convenient ways to collect multi-modal multi-view multi-scale datasets from human learning, such as brain imaging, brain-machine interface, human-machine system, and online learning platform. As a result, data mining from these datasets can be cheaper, more efficient, and more comprehensive in exploring brain cognition and education psychology for student learning activities.
On the one hand, researchers in psychology adopt AI models to find evidence from brain activities for learning behaviors, e.g., developing neural classifiers for students’ learning outcomes. On the other hand, researchers in neuroscience use AI models to analyze brain development changed by education for brain function discovery. Hence, AI in BCDE contributes to not only brain decoding but also scientific education.
In addition to the research plans for brain and intelligent education in the world, AI in BCDE will be an emerging research field and a successful multidisciplinary integration to explore the pathway of connecting brain science with learning science.
The goal of this research topic calls for contributions to bridging the BC with the DE from researchers in the fields of computational neuroscience, brain imaging, cognition science, machine learning, data mining, human-machine system, brain-machine interface, learning science, educational neuroscience, educational psychology, education game, intelligent education, and AI techniques.
We encourage submission related to data-driven AI research on BCED, including but not limited to:
1) Brain pattern recognition for learning behavior analysis
2) Brain image analysis for education observations
3) Brain cognition study for knowledge learning
4) Brain intelligence development in adolescence
5) Brain function analysis for learning, thinking, and plasticity
6) Brain-machine interface and human-machine system for digital education
7) AI in education and learning analytics
8) The intelligent education system and educational game research
9) Data mining and analysis in BCDE
10) Distributed and privacy-protected AI in BCDE
11) Graph construction and mining in BCDE
12) Knowledge tracing, concept cognition, and learning development in BCDE
In this digital era, modern devices provide convenient ways to collect multi-modal multi-view multi-scale datasets from human learning, such as brain imaging, brain-machine interface, human-machine system, and online learning platform. As a result, data mining from these datasets can be cheaper, more efficient, and more comprehensive in exploring brain cognition and education psychology for student learning activities.
On the one hand, researchers in psychology adopt AI models to find evidence from brain activities for learning behaviors, e.g., developing neural classifiers for students’ learning outcomes. On the other hand, researchers in neuroscience use AI models to analyze brain development changed by education for brain function discovery. Hence, AI in BCDE contributes to not only brain decoding but also scientific education.
In addition to the research plans for brain and intelligent education in the world, AI in BCDE will be an emerging research field and a successful multidisciplinary integration to explore the pathway of connecting brain science with learning science.
The goal of this research topic calls for contributions to bridging the BC with the DE from researchers in the fields of computational neuroscience, brain imaging, cognition science, machine learning, data mining, human-machine system, brain-machine interface, learning science, educational neuroscience, educational psychology, education game, intelligent education, and AI techniques.
We encourage submission related to data-driven AI research on BCED, including but not limited to:
1) Brain pattern recognition for learning behavior analysis
2) Brain image analysis for education observations
3) Brain cognition study for knowledge learning
4) Brain intelligence development in adolescence
5) Brain function analysis for learning, thinking, and plasticity
6) Brain-machine interface and human-machine system for digital education
7) AI in education and learning analytics
8) The intelligent education system and educational game research
9) Data mining and analysis in BCDE
10) Distributed and privacy-protected AI in BCDE
11) Graph construction and mining in BCDE
12) Knowledge tracing, concept cognition, and learning development in BCDE
Keywords: brain cognition, brain imaging, educational neuroscience and psychology, brain-machine interfaces, AI techniques, machine learning and data mining, learning science, intelligent education system
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
