Jaeseung+Jeong
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KAIST School of Transdisciplinary Studies Is Driving Innovation in Korean Education
<(From Left) Professor Jaeseung Jeong, haed of the School of Transdiciplinary Studies, Dr, Albert Chau, Vice President of Hong Kong Baptist University>
KAIST (President Kwang Hyung Lee) announced on the 24th of July that its School of Transdisciplinary Studies has been consistently showcasing the results of its experiments and practices for educational innovation both domestically and abroad.
On June 27, Professor Jaeseung Jeong, head of the School of Transdisciplinary Studies, was invited to speak at the “Pacific Asia Summit on Transdisciplinary Education 2025 (PASTE 2025)” held at Hong Kong Baptist University. He presented the Korean model of transdisciplinary education under the title “The Philosophy and Achievements of the KAIST School of Transdisciplinary Studies.”
In his talk, Professor Jeong pointed out the limitations of conventional education systems that rely on answer-centered evaluation, perfectionism, and competitiveness, claiming that they hinder creativity and integrative thinking. He then introduced the philosophy and operational practices of the School of Transdisciplinary Studies, which was established in 2019 to overcome these issues.
Professor Jeong outlined five key principles that define the school's educational philosophy: ①a broad and integrative academic foundation, ②student-driven and customized education, ③creativity and execution, ④a sense of social responsibility and global citizenship, and ⑤learning driven by intrinsic motivation and curiosity. He explained that students are admitted without a declared major, allowed to design their own learning plans, and evaluated under a P/NR system* that focuses on growth rather than competition.
*P/NR system: A non-competitive grading system led by KAIST’s School of Transdisciplinary Studies. Instead of traditional letter grades (A/B/C/Fail), students receive Pass (P) or No Record (NR), with the latter not appearing as a failure and not affecting GPA.
Professor Jeong emphasized, “This experiment at KAIST represents a new educational paradigm that values questions over knowledge, culture over structure, and inquiry over competition. Students are bridging academic learning and real-world practice by addressing societal challenges through technology, which could lead to a fundamental shift in global higher education.”
His presentation provided an opportunity to spotlight how KAIST’s experimental approach to nurturing transdisciplinary talent is pointing to new directions for the global education community beyond Korea.
< Hyungjoon Jang, a student at the School of Transdisciplinary Studies>
The achievements of KAIST’s transdisciplinary education model are also reflected in students’ academic accomplishments. Hyungjoon Jang, a student at the School of Transdisciplinary Studies, participated in a collaborative study led by his mentor, Professor Jaekyung Kim in the Department of Mathematical Sciences, along with researchers from Chungnam National University and the Institute for Basic Science (IBS). Their groundbreaking analytical method enables the accurate estimation of inhibition constants using only a single inhibitor concentration. The paper was published in the prestigious journal Nature Communications in June, with Jang listed as co–first author.
Jang played a leading role throughout the research process by developing the experimental methodology, creating a software package to support the method, drafting the manuscript, and engaging in peer review. He also effectively communicated mathematical and statistical models to pharmaceutical experts by mastering presentation techniques and visual explanation strategies, thereby setting a strong example of interdisciplinary collaboration.
He emphasized that “the School of Transdisciplinary Studies’ mentor system allowed regular research feedback and the systematic acquisition of essential knowledge and analytical skills through courses in biochemistry and computational neuroscience.”
This example demonstrates how undergraduate students at the School of Transdisciplinary Studies can take leading roles in cutting-edge interdisciplinary research.
The school’s educational philosophy is also reflected in students’ practical actions. Inseo Jeong, a current student and founder of the startup MPAge Inc., made a meaningful donation to help establish a creative makerspace in the school.
<Inseo Jeong, founder of MPAG>
Inseo Jeong explained that the decision was made to express gratitude for the knowledge gained and the mentorship received from professors, saying that at the School of Transdisciplinary Studies, she learned not only how to solve problems with technology but also how to view society, and that learning has helped her grow. She added, “The deep understanding of humanity and the world emphasized by Professor Jaeseung Jeong will be a great asset not only to entrepreneurs but to all students pursuing diverse paths,” expressing support for her fellow students.
Inseo Jeong collaborated for over two years with Professor Hyunwook Ka of the School of Transdisciplinary Studies on software research for individuals with hearing impairments. After numerous algorithm designs and experimental iterations, their work, which considered the social scalability of technology, was presented at the world-renowned CSUN Assistive Technology Conference held at California State University, Northridge. The project has filed for a patent under KAIST’s name.
※ Presentation title: Evidence-Based Adaptive Transcription for Sign Language Users
KAIST is now working to complete the makerspace on the third floor of the Administrative Annex (N2) in Room 314 with a size of approximately 33 m2 during the summer. The makerspace is expected to serve as a hands-on, integrative learning environment where various ideas can be realized and implemented, playing a key role in fostering students’ creative problem-solving and integrative thinking skills.
KAIST President Kwang Hyung Lee stated, “The School of Transdisciplinary Studies is both an experimental ground and a practical field for overcoming the limitations of traditional education and nurturing global talents with creative problem-solving skills and integrative thinking, which are essential for the future.” He added, “KAIST will continue to lead efforts to cultivate question-asking, inquiry-driven, transdisciplinary talents and propose new paradigms for education and research.”
2025.07.24 View 107 -
Decoding Brain Signals to Control a Robotic Arm
Advanced brain-machine interface system successfully interprets arm movement directions from neural signals in the brain
Researchers have developed a mind-reading system for decoding neural signals from the brain during arm movement. The method, described in the journal Applied Soft Computing, can be used by a person to control a robotic arm through a brain-machine interface (BMI).
A BMI is a device that translates nerve signals into commands to control a machine, such as a computer or a robotic limb. There are two main techniques for monitoring neural signals in BMIs: electroencephalography (EEG) and electrocorticography (ECoG).
The EEG exhibits signals from electrodes on the surface of the scalp and is widely employed because it is non-invasive, relatively cheap, safe and easy to use. However, the EEG has low spatial resolution and detects irrelevant neural signals, which makes it difficult to interpret the intentions of individuals from the EEG.
On the other hand, the ECoG is an invasive method that involves placing electrodes directly on the surface of the cerebral cortex below the scalp. Compared with the EEG, the ECoG can monitor neural signals with much higher spatial resolution and less background noise. However, this technique has several drawbacks.
“The ECoG is primarily used to find potential sources of epileptic seizures, meaning the electrodes are placed in different locations for different patients and may not be in the optimal regions of the brain for detecting sensory and movement signals,” explained Professor Jaeseung Jeong, a brain scientist at KAIST. “This inconsistency makes it difficult to decode brain signals to predict movements.”
To overcome these problems, Professor Jeong’s team developed a new method for decoding ECoG neural signals during arm movement. The system is based on a machine-learning system for analysing and predicting neural signals called an ‘echo-state network’ and a mathematical probability model called the Gaussian distribution.
In the study, the researchers recorded ECoG signals from four individuals with epilepsy while they were performing a reach-and-grasp task. Because the ECoG electrodes were placed according to the potential sources of each patient’s epileptic seizures, only 22% to 44% of the electrodes were located in the regions of the brain responsible for controlling movement.
During the movement task, the participants were given visual cues, either by placing a real tennis ball in front of them, or via a virtual reality headset showing a clip of a human arm reaching forward in first-person view. They were asked to reach forward, grasp an object, then return their hand and release the object, while wearing motion sensors on their wrists and fingers. In a second task, they were instructed to imagine reaching forward without moving their arms.
The researchers monitored the signals from the ECoG electrodes during real and imaginary arm movements, and tested whether the new system could predict the direction of this movement from the neural signals. They found that the novel decoder successfully classified arm movements in 24 directions in three-dimensional space, both in the real and virtual tasks, and that the results were at least five times more accurate than chance. They also used a computer simulation to show that the novel ECoG decoder could control the movements of a robotic arm.
Overall, the results suggest that the new machine learning-based BCI system successfully used ECoG signals to interpret the direction of the intended movements. The next steps will be to improve the accuracy and efficiency of the decoder. In the future, it could be used in a real-time BMI device to help people with movement or sensory impairments.
This research was supported by the KAIST Global Singularity Research Program of 2021, Brain Research Program of the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning, and the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education.
-PublicationHoon-Hee Kim, Jaeseung Jeong, “An electrocorticographic decoder for arm movement for brain-machine interface using an echo state network and Gaussian readout,” Applied SoftComputing online December 31, 2021 (doi.org/10.1016/j.asoc.2021.108393)
-ProfileProfessor Jaeseung JeongDepartment of Bio and Brain EngineeringCollege of EngineeringKAIST
2022.03.18 View 14459