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AI Gets a Private Tutor, Learning Human Preferences More Accurately
< Professor Junmo Kim and Ph.D. candidate Minchan Kwon, School of Electrical Engineering >
No matter how much data they learn, why do Artificial Intelligence (AI) models often miss the mark on human intent? Conventional "comparison learning," designed to help AI understand human preferences, has frequently led to confusion rather than clarity. A KAIST research team has now presented a new learning solution that allows AI to accurately learn human preferences even with limited data by assigning it a "private tutor."
On December17th, a research team led by Professor Junmo Kim of KAIST School of Electrical Engineering announced the development of "TVKD" (Teacher Value-based Knowledge Distillation), a reinforcement learning framework that significantly improves data efficiency and learning stability while effectively reflecting human preferences.
Existing AI training methods typically rely on collecting massive amounts of "preference comparison" data—simple structures like "A is better than B." However, this approach requires vast datasets and often causes the AI to become confused in ambiguous situations where the distinction is unclear.
To solve this problem, the research team proposed a method in which a ‘Teacher model’ that has first deeply understood human preferences delivers only the core information to a ‘Student model.’ This can be compared to a private tutor who organizes and teaches complex content, and the research team named this ‘Preference Distillation.’
The biggest feature of this technology is that instead of simply imitating ‘good or bad,’ it is designed so that the teacher model learns a ‘Value Function’ that numerically judges how valuable each situation is, and then delivers this to the student model. Through this, the AI can learn by making comprehensive judgments about ‘why this choice is better’ rather than fragmentary comparisons, even in ambiguous situations.
< Conceptual diagram of TVKD: After teaching the human preference dataset to the teacher model, learning proceeds by delivering the teacher's information and the dataset to the student model >
The core of this technology is twofold. First, by reflecting value judgments that consider the entire context into the student model, learning that understands the overall flow rather than fragmentary answers has become possible. Second, a technique was introduced to adjust learning importance according to the reliability of preference data. Clear data is significantly reflected in learning, while the influence of ambiguous or noisy data is reduced, allowing the AI to learn stably even in realistic environments.
As a result of the research team applying this technology to various AI models and conducting experiments, it showed more accurate and stable performance than methods previously known to have the best performance. In particular, it recorded achievements that stably outperformed existing top technologies in major evaluation indices such as MT-Bench and AlpacaEval.
Professor Junmo Kim said, “In reality, human preference data is not always sufficient or perfect,” and added, “This technology will allow AI to learn consistently even under such constraints, so it will be highly practical in various fields.”
< Performance comparison results for each task of MT-Bench. It can be confirmed that the proposed TVKD framework records generally higher scores than existing methods. >
< Visualization results of the Shaping term. The top tokens (converted into words) judged as important by the teacher model within the response are displayed in red, intuitively showing which tokens have a greater influence during the value-based alignment process. >
Ph.D. candidate Minchan Kwon from our university’s School of Electrical Engineering participated as the first author, and the research results were accepted at ‘NeurIPS 2025’, the most prestigious international conference in the field of artificial intelligence. The research was presented at a poster session on December 3, 2025 (US Pacific Time).
※ Paper Title: Preference Distillation via Value based Reinforcement Learning, DOI: https://doi.org/10.48550/arXiv.2509.16965
Meanwhile, this research was carried out with support from the Information & Communications Technology Planning & Evaluation (IITP) funded by the government (Ministry of Science and ICT) in 2024 (No. RS-2024-00439020, Development of Sustainable Real-time Multimodal Interactive Generative AI, SW Star Lab).
2025.12.17 View 1056 -
KAIST Welcomes the Class of 2026: “Play Boldly, Learn Deeply” - President Kwang-Hyung Lee
< President Kwang-Hyung Lee pictured with NYU exchange students >
KAIST announced on December 15th that it has delivered a congratulatory message to the successful applicants of the 2026 undergraduate early admissions, sharing the university’s unique educational philosophy of encouraging challenge and failure, as well as its vision for cultivating global talent.
For the 2026 undergraduate admissions, KAIST selected future scientific leaders based on its core values and talent ideals: Creativity, Challenge, and Caring. KAIST plans to strengthen education focused on nurturing convergent talent who can cross disciplinary boundaries. The recent upward trend in applications to KAIST reflects the growing importance of scientific talent who will lead national competitiveness amidst intense global competition in AI, semiconductors, space, and biotechnology.
In his congratulatory message, President Kwang Hyung Lee emphasized, “KAIST is a place where you can play and study to your heart's content with friends, start your own business, and even experience failure. KAIST is a ‘playground for eccentrics’ where you can try anything.”
He specifically introduced a challenge-oriented academic culture, stating, “Do not fear failure. If you organize and share your experiences of failure well, you might even receive a ‘Failure Award.’”
President Lee further stressed, “KAIST is the perfect school for students who want to blaze new trails through creativity and inquiry, and for those who wish to change the world. If your goal is simply to get an ‘A’ in every subject or to secure a stable job, you do not need to come here. However, if you are a student who prefers defining your own problems over doing what others tell you and wants to challenge yourself beyond established frameworks, you must come to KAIST.”
He also highlighted the free, student-led environment by stating, “For a KAISTian, the only limit to challenge is imagination,” adding, “During my tenure as President, I have never once rejected an idea proposed by students.”
Regarding the global educational environment, President Lee explained, “KAIST is no longer just a domestic university; it is a platform where you can study, research, and be active on the world stage. We actively support students’ global experiences through the joint campus operation with New York University (NYU), the establishment of a Silicon Valley campus, and exchange programs with over 100 overseas universities.”
Meanwhile, to lead the AI era, KAIST recently established the nation’s first AI College and is building a full-scale education and research system covering all fields of artificial intelligence. The AI College plans to systematically foster next-generation AI leaders through a curriculum linked from undergraduate to graduate levels.
In addition, KAIST is strengthening education in humanities, culture, and the arts alongside science and technology. The university operates seven humanities and social science minor programs—Digital Humanities & Social Sciences, Economics, Culture Technology, Intellectual Property, Science & Technology Policy, Entrepreneurship, and Future Strategy. It also expands students' imagination and creativity through on-campus art museums, numerous galleries, and regular performances and cultural events.
Furthermore, KAIST encourages challenge and balanced growth through the “Mountaineering Scholarship,” which provides up to 700,000 KRW annually to students who complete designated hiking courses, regardless of grades or income level.
President Lee concluded his message of support by saying, “My heart is already racing at the thought of pioneering the 21st-century future with all of you. I look forward to seeing you grow into ‘stars,’ each with your own unique color, and shine on the global stage.”
< President Kwang Hyung Lee performing with the student lab club 'Gootos' at Innovate Korea 2024 >
2025.12.15 View 1506 -
AI-Engineered "Nasal Spray Antiviral Platform" Developed to Block Flu and COVID-19
<(From Left) Professor Hyun Jung Chung, Professor Ho Min Kim, Professor Ji Eun Oh>
<(From Left) Dr. Seungju Yang, Dr. Jeongwon Yun, Ph.D candidate Jae Hyuk Kwon>
Respiratory viruses that have diverse strains and mutate rapidly, such as influenza and COVID-19, are difficult to block perfectly with vaccines alone. To solve this problem, KAIST's research team has successfully developed a nasal (intranasal) antiviral platform using AI technology to overcome the existing limitations of interferon-lambda treatments—namely, being "weak against heat and disappearing quickly from the nasal mucosa."
KAIST announced on December 15th that a joint research team—consisting of Professor Ho Min Ktim and Professor Hyun Jung Chung from the Department of Biological Sciences, and Professor Ji Eun Oh from the Graduate School of Medical Science and Engineering used AI to stably redesign the interferon-lambda protein and combined it with a delivery technology that ensures effective diffusion and long-term retention in the nasal mucosa, thereby implementing a universal prevention technology for various respiratory viruses.
Interferon-lambda is an innate immune protein produced by the body to block viral infections, playing a crucial role in stopping respiratory viruses like the common cold, flu, and COVID-19. However, when formulated as a treatment for nasal administration, its actual efficacy was limited by its vulnerability to heat, degrading enzymes, mucus, and ciliary motion.
The research team used AI protein design technology to precisely reinforce the structural weaknesses of interferon-lambda.
First, they significantly increased stability by changing the loose "loop" structures of the protein—which were prone to instability—into rigid "helix" structures that lock in place like a firm spring.
Additionally, to prevent "aggregation" (proteins sticking together to form lumps), they applied "surface engineering" to make the surface more water-compatible. They also introduced "glycoengineering," adding sugar chain (glycan) structures to the protein surface to make it even more robust and stable.
As a result, the newly produced interferon-lambda showed a massive improvement in stability, surviving for two weeks 50℃ and demonstrated the ability to diffuse rapidly even through thick nasal mucus.
The research team further protected the protein by encapsulating it in microscopic "nanoliposomes" and coated the surface with "low-molecular-weight chitosan." This significantly enhanced "mucoadhesion," allowing the treatment to stick to the nasal lining for an extended period.
When this delivery platform was applied to animal models infected with influenza, a powerful inhibitory effect was confirmed, with the virus level in the nasal cavity decreasing by more than 85%.
This technology is a mucosal immune platform that can block viral infections in their early stages simply by spraying it into the nose. It is expected to be a new therapeutic strategy that can respond quickly not only to seasonal flu but also to unexpected new or mutant viruses.
Professor Ho Min Kim stated, "Through AI-based protein design and mucosal delivery technology, we have simultaneously overcome the stability and retention time limitations of existing interferon-lambda treatments. This platform, which is stable at high temperatures and stays in the mucosa for a long time, is an innovative technology that can be used even in developing countries lacking strict cold-chain infrastructure. It also has great scalability for developing various treatments and vaccines." He added, "This is a meaningful achievement resulting from multidisciplinary convergence research, covering everything from AI protein design to drug delivery optimization and immune evaluation through infection models."
This research involved Dr. Jeongwon Yun from the KAIST InnoCORE (AI-Co-Research & Eudcation for innovative Drug Institute, AI-CRED Institute) Dr. Seungju Yang from the Department of Biological Sciences, and PhD student Jae Hyuk Kwon from the Graduate School of Medical Science and Engineering as co-first authors. The results were published consecutively in the renowned international journals Advanced Science (Nov 20) and Biomaterials Research (Nov 21).
Paper 1: Computational Design and Glycoengineering of Interferon-Lambda for Nasal Prophylaxis against Respiratory Viruses, Advanced Science, DOI: 10.1002/advs.202506764
Paper 2: Intranasal Nanoliposomes Delivering Interferon Lambda with Enhanced Mucosal Retention as an Antiviral, Biomaterials Research, DOI: 10.34133/bmr.0287
This research was conducted with support from the KAIST InnoCORE Program, Mid-Career Researcher Support Program and the Bio-Medical Technology Development Program through the National Research Foundation of Korea (NRF), Healthcare Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), the KAIST Convergence Research Institute Operation Program, and the Institute for Basic Science (IBS).
2025.12.15 View 1450 -
Uncovering brain’s secret to stable yet flexible learning – paving the way for human-like AI
<(From Left) Professor Sang Wan Lee, Ph.D candidate Yoondo Sung, (Upper Left) Dr. Mattia Rigotti>
Humans possess a remarkable balance between stability and flexibility, enabling them to quickly establish new plans and adjust goals even in the face of sudden changes. However, "Model-Free reinforcement learning," which is widely used in robotics and exemplified by AlphaGo’s famous match against Lee Sedol, struggle to achieve these two capabilities simultaneously. KAIST's research team has discovered that the secret lies in the unique information processing method within the prefrontal cortex, a principle that could serve as the foundation for developing "Brain-like AI” that is both flexible and stable.
KAIST announced on December 14th that a research team led by Professor Sang Wan Lee from the Department of Brain and Cognitive Sciences, in collaboration with IBM AI Research, has deciphered how the human brain manages goal changes in uncertain situations, suggesting a new direction for next-generation reinforcement learning.
The research team highlighted a critical limitation of current reinforcement learning models: they lose the balance between flexibility for goals pursuit and stability in uncertain environments. Humans, however, achieve both simultaneously. The team hypothesized that this difference arises from how the prefrontal cortex represents information.
Using functional MRI (fMRI) experiments, reinforcement learning models, and advanced AI analyses, the team revealed that the human prefrontal cortex has a unique embedding structure that represents "goal information" and "uncertainty information" separately to prevent interference. Individuals with more distinct separation between these channels were able to adapt strategies when goals shifted, while maintaining stable judgment despite environmental uncertainty. The team likened this mechanism to "multiplexing" in communication technology, where multiple signals are transmitted simultaneously without interference.
In this way, the human prefrontal cortex operates through two "channels": one that sensitively tracks goal changes to ensure flexibility in decision-making, and another that isolate environmental uncertainty to maintain stable judgment.
An interesting point is that the prefrontal cortex goes beyond simple executing control guided by the first channel; it uses the second channel to actually choose which learning strategy to use depending on the situation.
This demonstrates the brain’s "meta-learning capabilities," meaning it learns not only what to learn but also how to learn – by choosing which learning strategy to use. This is why humans remain resilient in constantly changing situations.
The implication of this research extend across various fields, including the analysis of individual reinforcement and meta-learning abilities, personalized education design, cognitive diagnosis, and human-computer interaction (HCI). Moreover, embedding brain-inspired representation structures into AI could lead to "brain-like thinking AI", allowing AI to better understand human intentions and values, reducing dangerous judgments, and enabling safer cooperation with humans.
<Figure 1. Balance between Flexibility and Stability in Humans and AI>
<Figure 2. Topological Structure of Goal Representation in the Prefrontal Cortex and Environmental Uncertainty Information>
Lead researcher Professor Sang Wan Lee emphasized the significance of the findings: "This study clarifies the brain's fundamental operating principles—from flexibly following changing goals to stably establishing plans—from an AI perspective. These principles will serve as a core foundation for next-generation AI, allowing it to adapt like a human and learn more safely and intelligently."
This study featured PhD candidate Yoondo Sung as the first author and Dr. Mattia Rigotti of IBM AI Research as the second author, with Professor Sang Wan Lee serving as the corresponding author. The research results were published on November 26 in the international academic journal Nature Communications.
(Paper Title: Factorized embedding of goal and uncertainty in the lateral prefrontal cortex guides stably flexible learning / DOI: 10.1038/s41467-025-66677-w)
Notably, this research was conducted with support from the "Frontier R&D Project" of the Ministry of Science and ICT.
2025.12.15 View 379 -
KAIST-KakaoBank Speeds Up 'Explainable AI' by 11 Times: "Boosts Financial AI Reliability
< (From left) Professor Jaesik Choi of the Kim Jaechul Graduate School of AI, Ph.D candidate Chanwoo Lee, Ph.D candidate Youngjin Park >
The research team led by Professor Jaesik Choi of KAIST's Kim Jaechul Graduate School of AI, in collaboration with KakaoBank Corp, announced that they have developed an accelerated explanation technology that can explain the basis of an Artificial Intelligence (AI) model's judgment in real-time. This research achievement significantly increases the practical applicability of Explainable Artificial Intelligence (hereinafter XAI) technology in fields requiring real-time decision-making, such as financial services, by achieving an average processing speed 8.5 times faster, and up to 11 times faster, than existing explanation algorithms for AI model predictions.
In the financial sector, a clear explanation for decisions made by AI systems is essential. Especially in services directly related to customer rights, such as loan screening and anomaly detection, regulatory demands to transparently present the basis for the AI model's judgment are increasingly stringent. However, conventional Explainable Artificial Intelligence (XAI) technologies required the repeated calculation of hundreds to thousands of baselines to generate accurate explanations, resulting in massive computational costs. This was a major factor limiting the application of XAI technology in real-time service environments.
To address this issue, Professor Choi's research team developed the 'ABSQR (Amortized Baseline Selection via Rank-Revealing QR)' framework for accelerating explanation algorithms. ABSQR noticed that the value function matrix generated during the AI model explanation process has a low-rank structure. It introduced a method to select only a critical few baselines from the hundreds available. This drastically reduced the computation complexity, which was previously proportional to the number of baselines, to be proportional only to the number of selected critical baselines, thereby maximizing computational efficiency while maintaining explanatory accuracy.
Specifically, ABSQR operates in two stages. The first stage systematically selects important baselines using Singular Value Decomposition (SVD) and Rank-Revealing QR decomposition techniques. Unlike existing random sampling methods, this is a deterministic selection method aimed at preserving information recovery, which guarantees the accuracy of the explanation while significantly reducing computation. The second stage introduces an amortized inference mechanism, which reuses the pre-calculated weights of the baselines through cluster-based search, allowing the system to provide an explanation for the model's prediction result in real-time service environments without repeatedly evaluating the model. The research team verified the superiority of ABSQR through experiments on various real-world datasets. Tests on standard datasets across five sectors—finance, marketing, and demographics—showed that ABSQR achieved an average processing speed 8.5 times faster than existing explanation algorithms that use all baselines, with a maximum speed improvement of over 11 times. Furthermore, the degradation of explanatory accuracy due to speed acceleration was minimized, maintaining up to 93.5% of the explanation accuracy compared to the baseline algorithm. This level is sufficient to meet the explanation quality required in real-world applications.
< ABSQR Framework Overview. (1) The baseline selection stage utilizes the low-rank structure of the value function matrix to select only a small number of key baselines, and (2) the accelerated search stage reuses the pre-calculated baseline weight coefficients based on clusters. This dramatically reduces the computation complexity, which was proportional to the number of baselines, to be proportional only to the number of selected key baselines. >
A KakaoBank official stated, "We will continue relentless research and development to enhance the reliability and convenience of financial services and introduce innovative financial technologies that customers can experience." Chanwoo Lee and Youngjin Park, co-first authors from KAIST, explained the significance of the research: "This methodology solves the crucial acceleration problem for real-time application in the financial sector, proving that it is possible to provide users with the reasons behind a learning model's decision in real-time." They added, "This research provides new insights into what constitutes unnecessary computation and the selection of important baselines in explanation algorithms, practically contributing to the improvement of explanation technology efficiency." This research, co-authored by PhD candidates Chanwoo Lee and Youngjin Park from the KAIST Kim Jaechul Graduate School of AI, and researchers Hyeongeun Lee and Yeeun Yoo from the KakaoBank Financial Technology Research Institute, was presented on November 12 at the 'CIKM 2025 (ACM International Conference on Information and Knowledge Management)', the world's highest-authority academic conference in the field of information and knowledge management. ※ Paper Title: Amortized Baseline Selection via Rank-Revealing QR for Efficient Model Explanation
※ Author Information:
※ Author Information: DOI: https://doi.org/10.1145/3746252.3761036
Co-First Authors: Chanwoo Lee (KAIST Kim Jaechul Graduate School of AI), Youngjin Park (KAIST Kim Jaechul Graduate School of AI), Hyeogeun Lee (KakaoBank), Yeeun Yoo (KakaoBank)
Co-Authors: Daehee Han (KakaoBank), Junho Choi (KAIST Kim Jaechul Graduate School of AI), Kunhyung Kim (KAIST Kim Jaechul Graduate School of AI)
Corresponding Authors: Nari Kim (KAIST Kim Jaechul Graduate School of AI), Jaesik Choi (KAIST Kim Jaechul Graduate School of AI)
Meanwhile, this research achievement was conducted through KakaoBank's industry-academia research project 'Advanced Research on Explainable Artificial Intelligence Algorithms in the Financial Sector' and the Ministry of Science and ICT/Institute for Information & Communications Technology Planning and Evaluation (IITP) supported project 'Development of Explainable Artificial Intelligence Technology Providing Explainability in a Plug-and-Play Manner and Verification of Explanation Provision for AI Systems.'
2025.12.12 View 1349 -
Robot Valley Project Activation of the Korean style Robot and AI Startup Ecosystem Fully Underway
< From left: Top Excellence Award winner Robolight (Pre-startup Founder Han-seol Choi), Top Excellence Award winner Coils (CEO Seong-ryeol Heo), Professor Jung Kim of KAIST, Grand Prize winner Noman (CEO Jung-wook Moon), Professor Kyoungchul Kong of KAIST, CEO Dae-hee Park of Daejeon Creative Economy Innovation Center, Excellence Award winner Gigaflops (CEO Min-tae Kim), Excellence Award winner BLUE APEX (Pre-startup Founder Na-hyeon Kwon) >
KAIST announced on December 10th that KAIST Holdings (CEO Hyeonmin Bae), a specialized technology commercialization investment institution, successfully held the '2025 KAIST Hu-Robotics Startup Cup' on the 9th at the main building of Daejeon Startup Park. This was held as part of the Robot Valley Project, aiming to discover and foster promising startup teams in the robotics field and establish a robot scale-up ecosystem based on a technology platform.
This competition was conducted as a core program of the Robot Valley Project (Deep-Tech Scale-up Valley Fostering Project), which is promoted by the Ministry of Science and ICT and supported by Daejeon Metropolitan City. The competition proceeded through a meet-up day with KAIST Mechanical Engineering researchers, robotics companies like Angel Robotics and Twinny, and startup experts such as Bluepoint, leading to the final round. Throughout this process, a support system for the scale-up of robot startups was established, linking technology verification, strengthening entrepreneurial capabilities, and investment linkage.
KAIST Holdings and the Deep-Tech Valley Project Group (hereinafter referred to as the Project Group) stated that this competition marks the beginning of 'establishing a Korean-style Robot and AI startup ecosystem.' Their goal through the Robot Valley Project is to create a Korean-style robot scale-up ecosystem centered around Daejeon and KAIST, and furthermore, to build a technology circulation structure utilizing verified technology platforms.
KAIST has produced successful scale-up cases in the robotics field, such as Rainbow Robotics and Angel Robotics. However, the recent robotics industry has seen a rapid increase in technological difficulty due to the convergence of mechanical engineering, AI, and control software, creating structural limitations for early-stage founders to challenge alone.
To solve this, the Project Group proposed the 'Scale-up Valley Construction Strategy,' which opens up the verified technologies of established senior companies to junior founders. This strategy focuses on supporting startups to concentrate on developing market-ready robot services and applications on top of verified technology platforms, rather than consuming excessive time on developing basic hardware like motors and controllers.
The Angel Robotics technology platform, presented as the core underlying technology of this strategy, consists of actuators, control modules, and core software. KAIST plans to gradually open up these foundational technologies for use by early-stage startup teams.
The Project Group emphasized that enabling startup teams to utilize such technology platforms from the initial stage is the core infrastructure for accelerating the Korean-style robot startup ecosystem.
A total of 21 teams participated in this competition, including pre-startup founders (Track A) and early-stage startups established within 3 years (Track B), all possessing human-centered robotics technology and convergence business models.
After fierce preliminaries, 8 teams advanced to the final round, and a total of 5 teams were finally selected: one Grand Prize winner, two Choi Woo-sung (Top Excellence Award) winners, and two Excellence Award winners.
The Grand Prize was awarded to 'Noman' for proposing an integrated system for a strawberry farm work robot and a rotating vertical cultivation module.
The Woo-sung Choi (Top Excellence Award) went to 'Robolight' and 'Coils.'
The Excellence Award was awarded to BLUE APEX and Gigaflops.
Professor Jung Kim, Head of the KAIST Mechanical Engineering Department and General Manager of the Robot Valley Project, said, "This competition has become the starting point for discovering future robot unicorns. For the next three years, we will continue to provide practical support for the growth of robot startups, and KAIST will play a leading role in building and expanding the deep-tech robot ecosystem centered in Daejeon."
< Group Photo of Award Winners >
Meanwhile, this competition was jointly hosted and organized by the Ministry of Science and ICT, Daejeon Metropolitan City, and the Research and Business Development Special Zone Foundation, as well as startup support organizations including KAIST, KAIST Holdings, Daejeon Technopark, and Daejeon Creative Economy Innovation Center.
2025.12.10 View 1421 -
KAIST Predicts Human Group Behavior with AI! 1st Place at the World’s Top Conference… Major Success after 23 Years
<(From Left) Ph.D candidate Geon Lee, Ph.D candidate Minyoung Choe, M.S candidate Jaewan Chun, Professor Kijung Shin, M.S candidate Seokbum Yoon>
KAIST (President Kwang Hyung Lee) announced on the 9th of December that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed a groundbreaking AI technology that predicts complex social group behavior by analyzing how individual attributes such as age and role influence group relationships.
With this technology, the research team achieved the remarkable feat of winning the Best Paper Award at the world-renowned data mining conference “IEEE ICDM,” hosted by the Institute of Electrical and Electronics Engineers (IEEE). This is the highest honor awarded to only one paper out of 785 submissions worldwide, and marks the first time in 23 years that a Korean university research team has received this award, once again demonstrating KAIST’s technological leadership on the global research stage.
Today, group interactions involving many participants at the same time—such as online communities, research collaborations, and group chats—are rapidly increasing across society. However, there has been a lack of technology that can precisely explain both how such group behavior is structured and how individual characteristics influence it at the same time.
To overcome this limitation, Professor Kijung Shin’s research team developed an AI model called “NoAH (Node Attribute-based Hypergraph Generator),” which realistically reproduces the interplay between individual attributes and group structure.
NoAH is an artificial intelligence that explains and imitates what kinds of group behaviors emerge when people’s characteristics come together. For example, it can analyze and faithfully reproduce how information such as a person’s interests and roles actually combine to form group behavior.
As such, NoAH is an AI that generates “realistic group behavior” by simultaneously reflecting human traits and relationships. It was shown to reproduce various real-world group behaviors—such as product purchase combinations in e-commerce, the spread of online discussions, and co-authorship networks among researchers—far more realistically than existing models.
< The process of generating group interactions using NoAH >
Professor Kijung Shin stated, “This study opens a new AI paradigm that enables a richer understanding of complex interactions by considering not only the structure of groups but also individual attributes together,” and added, “Analyses of online communities, messengers, and social networks will become far more precise.”
This research was conducted by a team consisting of Professor Kijung Shin and KAIST Kim Jaechul Graduate School of AI students: master’s students Jaewan Chun and Seokbum Yoon, and doctoral students Minyoung Choe and Geon Lee, and was presented at IEEE ICDM on November 18.
※ Paper title: “Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes” Original paper: https://arxiv.org/abs/2509.21838
< Photo from the award ceremony held on November 14 at the International Spy Museum in Washington, D.C.>
Meanwhile, including this award-winning paper, Professor Shin’s research team presented a total of four papers at IEEE ICDM this year. In addition, in 2023, the team also received the Best Student Paper Runner-up (4th place) at the same conference.
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-202400457882, AI Research Hub Project) (RS-2019-II190075, Artificial Intelligence Graduate School Program (KAIST)) (No. RS-2022-II220871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration).
2025.12.09 View 1743 -
KAIST Drives National Competitiveness with a Dual-Impact Model for AI Research and Regional Innovation
<Photo of KAIST Students>
KAIST announced on December 9th that it will accelerate the nurturing of world-class scientific talent and regional balanced development. This follows the government's recent announcement on 'Leaping to a Science and Technology Powerhouse, the Republic of Korea, Where People Dream of Becoming Science and Technology Professionals Again (Nov. 7),' which explicitly named the four major science and technology institutes, including KAIST, as AX (AI Transformation) innovation hubs and key leading institutions for regional innovation.
This move aligns with the policy direction of President Jae-myung Lee. On November 4th, President Jae-myung Lee stated in a Cabinet meeting, "STEM talent is the core of national competitiveness," adding that "the increase in applicants for early admissions to the four major science and technology institutes is a very desirable phenomenon for the nation's future." In particular, the President requested that the government "actively seek concrete policies, such as expanding the allowance for transfers between STEM fields, increasing budget support, securing excellent faculty, and upgrading research and education infrastructure, because science and technology institutes can also significantly contribute to regional balanced development."
KAIST President Kwang Hyung Lee stated, "Strengthening AI research capabilities and regional balanced development is a Dual-Impact Model for AI Research and Regional Innovation that boosts national competitiveness." He confirmed that through the government's policy direction, the innovation philosophy KAIST has pursued—that 'the region is national competitiveness'—has been established as a core national direction.
In reality, KAIST continues to firmly play a central role in nurturing the talent that sustains South Korea's science and technology sector, even amid the deepening phenomenon of students flocking to medical schools. The increase in early admission applicants to the four science and technology institutes proves the successful establishment of education and research foundations where students can choose the dream of becoming science and technology professionals instead of doctors. To accelerate this trend, KAIST is focusing on establishing a National AI Research Lab and pioneering the next-generation AI research paradigm with the goal of becoming one of the top three AI powerhouses (G3) globally.
Our university was selected not only to lead the development of the next-generation bio-AI model 'K-Fold'—which surpasses Google DeepMind—and as a key participating institution in the Lunit consortium, but also as a core research team in the national AI flagship project, the 'Generative AI Leading Talent Cultivation Program.' Through discovering research topics that reflect diverse technological demands from industries, nurturing advanced AI talent, and demonstrating research outcomes in industrial settings, KAIST is being reborn as a field-ready leader guiding the AI Transformation (AX) across all of South Korea's industries.
KAIST's AI research competitiveness has also been officially recognized overseas. NVIDIA CEO Jensen Huang personally introduced KAIST as an "Amazing University" during his keynote speech at the 2025 APEC CEO Summit (Oct. 31), highly evaluating KAIST's world-class research capabilities and global collaboration potential.
Regional innovation is also gaining momentum. Our university is expanding physical AI-based research infrastructure in regions like Jeonbuk and Gyeongnam, centered around its main campus in Daejeon. Through the AI and robot-based 'Robot Valley Project' and the 'Global Innovation Startup Growth Hub Project,' in cooperation with Daejeon City, KAIST is supporting the advancement of local industries and the growth and global expansion of startups.
<ANGEL SUIT, a gait-training robot>
In particular, Sovagen—a bio-company founded on the technology of Professor Jeong Ho Lee of the KAIST Graduate School of Medical Science—recently succeeded in an overseas technology transfer of an RNA new drug for epilepsy valued at 750 billion KRW, proving a virtuous cycle model of innovation where university research translates into actual industry success.
Furthermore, the foundation for future talent development is being strengthened through efforts like promoting a culture of challenging research via the 'Failure Lab,' and early nurturing of outstanding talent through the 'Junior KAIST' and '3+4 TUBE Programs.' While setting the direction for regional university innovation through the specialized and performance-centric 'KAIST Model,' the university is also taking the lead in popularizing science and fulfilling its social responsibilities.
President Kwang Hyung Lee emphasized, "We will continue to pursue the expansion of the AI research budget and the establishment of international joint research infrastructure through close cooperation with the government." He concluded, "We will cultivate young talents who have chosen the future to be the main players in South Korean science and technology, fulfilling our central role in the 'AI Powerhouse Republic of Korea,' where the nation and the regions grow together."
2025.12.09 View 1527 -
KAIST Removes 99.9% of Ultrafine Dust Using Nano Water Droplet Technology
<(From Left) Ph.D candidate Sungyoon Woo, Professor Il-Doo Kim, Professor Seung S.Lee, Ph.D candiate Jihwan Chae, Researcher Jiyeon Yu, (Upper Right) Dr. Yujang Cho>
A KAIST research team has drawn attention by developing a new water-based air purification technology that combines “nano water droplets that capture dust” with a “nano sponge structure that autonomously draws up water,” enabling dust removal using nano water droplets without filters, self-supplied water operation, and long-term, quiet, and safe performance.
KAIST (President Kwang Hyung Lee) announced on the December 8 that a joint research team led by Professor Il-Doo Kim of the Department of Materials Science and Engineering and Professor Seung S. Lee of the Department of Mechanical Engineering developed a new water electrospray–based air purification device that rapidly removes ultrafine dust without filters, generates no ozone, and operates with ultra-low power consumption.
The research team confirmed that this device overcomes the limitations of conventional air purifiers by eliminating the need for filter replacement, producing no ozone, and removing even extremely fine ultrafine dust as small as PM0.3 (diameter 0.3 μm), which is about 1/200 the thickness of a human hair, within a short time. In addition, it demonstrated high stability and durability without performance degradation even during long-term use.
This device was created by combining Professor Seung S. Lee’s “ozone-free water electrospray” technology with Professor Il-Doo Kim’s “hygroscopic nanofiber Emitter” technology.
Inside the device are a high-voltage electrode, a nanofiber absorber that autonomously draws up water, and polymer microchannels that transport water via capillary action. Thanks to this structure, a self-pumped configuration is achieved in which water is automatically supplied without a pump, enabling stable long-term water electrospray operation.
Tests conducted by the research team in a 0.1 m3 experimental chamber showed that the device removed 99.9% of various particles in the PM0.3–PM10 range within 20 minutes. In particular, it exhibited outstanding performance by removing 97% of PM0.3 ultrafine dust, which is difficult to eliminate using conventional filter-based air purifiers, within just 5 minutes.
Even after 30 consecutive tests and 50 hours of continuous operation, the device operated stably without performance degradation, and its power consumption was approximately 1.3 W, which is lower than that of a smartphone charger and only about 1/20 that of conventional HEPA (High Efficiency Particulate Air) filter–based air purifiers.
In addition, because there is no filter, there is no pressure loss in airflow and almost no noise is generated.
This technology maintains high-efficiency purification performance while generating no ozone at all, presenting the potential for a next-generation eco-friendly air purification platform.
In particular, with advantages such as elimination of filter replacement costs, ultra-low power operation, and secured long-term stability, it is expected to expand into various fields including indoor environments as well as automotive, cleanroom, portable, and wearable air purification modules.
Commercialization of this technology is currently underway through A2US Co., Ltd., a university spin-off company from Professor Seung S. Lee’s laboratory.
A2US Co., Ltd. won a CES 2025 Innovation Award and plans to launch a portable air purifier product in 2026. The product is equipped not only with fine dust removal using nano water droplets but also with odor removal and pathogen sterilization functions.
<Figure1.Design and Operating Mechanism of a Miniature Air-Purification Device Based on Cone-Jet Water Electrospray Using a Self-Pumping Hygroscopic (PVA–PAA–MMT) Nanofiber Membrane (PPM-NFM) Emitter.>
<Figure 2. (a) Schematic of the Self-Pumping Hygroscopic Nanofiber Membrane (PPM-NFM) Emitter, and (b) Corresponding Photograph and Surface Scanning Microscopy Images.>
This research was conducted with Jihwan Chae (Ph.D. candidate, Department of Mechanical Engineering, KAIST) and Yujang Cho (Ph.D., Department of Materials Science and Engineering, KAIST) as co–first authors, and with Professor Seung S. Lee (Department of Mechanical Engineering) and Professor Il-Doo Kim (Department of Materials Science and Engineering) as corresponding authors. The research results were published on November 14 in the international journal Advanced Functional Materials (AFM), published by Wiley, a world-renowned publisher in materials science and nanotechnology.
※ Paper title: “Self-Pumped Hygroscopic Nanofiber Emitter for Ozone-Free Water Electrospray-Based Air Purification,” DOI: 10.1002/adfm.202523456
This research was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, and the KAIST–MIT Future Energy Frontier Research Center (AI-robotics–based energy materials innovation) program.
2025.12.08 View 1709 -
KAIST, Production Temperature ↓ by 500°C, Power Output ↑ 2x… Next-Generation Ceramic Electrochemical Cell Reborn
<(Top row, from left) Professor Kang Taek Lee, Ph.D candidate Yejin Kang, Dr. Dongyeon Kim, (Bottom row, from left) M.S candidate Mincheol Lee, Ph.D candidate Seeun Oh, Ph.D candidate Seungsoo Jang, Ph.D candidate Hyeonggeun Kim>
As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world.
KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave + vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components.
The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance.
As power demand surges in the AI era, the “protonic ceramic electrochemical cell (PCEC),” which can simultaneously produce electricity and hydrogen, is gaining attention as a next-generation energy technology. However, this cell has faced the technical limitation of requiring an ultra-high production temperature of 1,500°C. A KAIST research team has succeeded in establishing a new manufacturing process that lowers this limit by more than 500°C for the first time in the world.
KAIST (President Kwang Hyung Lee) announced on the 4th of December that Professor Kang Taek Lee’s research team in the Department of Mechanical Engineering developed a new process that enables the fabrication of high-performance protonic ceramic electrochemical cells at temperatures more than 500°C lower than before, using “microwave + vapor control technology” that leverages microwave heating principles and the diffusion environment of chemical vapor generated from specific chemical components.
The electrolyte—the key material of protonic ceramic electrochemical cells—contains barium (Ba), and barium easily evaporates at temperatures above 1,500°C, which has been the main cause of performance degradation. Therefore, the ability to harden the ceramic electrolyte at a lower temperature has been the core issue that determines cell performance.
To solve this, the research team devised a new heat-treatment method called “vapor-phase diffusion.” This technique places a special auxiliary material (a vapor source) next to the cell and irradiates it with microwaves to quickly diffuse vapor. When the temperature reaches approximately 800°C, the vapor released from the auxiliary material moves toward the electrolyte and tightly bonds the ceramic particles. Thanks to this technology, a process that previously required 1,500°C can now be completed at just 980°C. In other words, a world-first ceramic electrochemical cell fabrication technology has been created that produces high-performance electricity at a “low temperature” without damaging the electrolyte.
A cell fabricated with this process produced 2 W of power stably from a 1 cm² cell (roughly the size of a fingernail) at 600°C and generated 205 mL of hydrogen per hour at 600°C (about the volume of a small paper cup, among the highest in the industry). It also maintained stability without performance degradation during 500 hours of continuous operation.
In other words, this technology reduces the production temperature (−500°C), lowers the operating temperature (600°C), doubles performance (2 W/cm²), and extends the lifespan (500-hour stability), achieving world-class performance in ceramic cell technology.
The research team also enhanced the reliability of the technology by using digital twins (virtual simulations) to analyze gas-transport phenomena occurring in the microscopic internal structure of the cell − phenomena that are difficult to observe in actual experiments.
<Figure 1. (a) Schematic of the vapor-diffusion-based process; (b) Surface microstructure of the electrolyte; (c) Internal barium composition ratio of the electrolyte according to processing conditions; (d) Comparison of power-generation performance with previous studies>
< Figure 2. (a) Three-dimensional reconstructed image of the protonic ceramic electrochemical cell fuel electrode according to processing conditions (b) Pore structure (c) Gas-transport simulation results >
Professor Kang Taek Lee emphasized, “This study is the world’s first case of using vapor to lower the heat-treatment temperature by more than 500°C while still producing a high-performance, high-stability cell.” He added, “It is expected to become a key manufacturing technology that addresses the power challenges of the AI era and accelerates the hydrogen society.”
Dongyeon Kim (KAIST PhD) and Yejin Kang (KAIST PhD candidate) participated as co–first authors. The research results were published in Advanced Materials (IF: 26.8), one of the world’s leading journals in energy and materials science, and were selected as the Inside Front Cover article on October 29.
(Paper title: “Sub-1000°C Sintering of Protonic Ceramic Electrochemical Cells via Microwave-Driven Vapor Phase Diffusion,” DOI: https://doi.org/10.1002/adma.202506905)
This research was supported by the MSIT’s Mid-career Researcher Program and the H2 Next Round Program.
2025.12.05 View 1517 -
How Does AI Think? KAIST Achieves First Visualization of the Internal Structure Behind AI Decision-Making
<(From Left) Ph.D candidate Daehee Kwon, Ph.D candidate Sehyun lee, Professor Jaesik Choi>
Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge.
KAIST (President Kwang Hyung Lee) announced on the 26th of November that Professor Jaesik Choi’s research team at the Kim Jaechul Graduate School of AI has developed a new explainable AI (XAI) technology that visualizes the concept-formation process inside a model at the level of circuits, enabling humans to understand the basis on which AI makes decisions.
The study is evaluated as a significant step forward that allows researchers to structurally examine “how AI thinks.”
Inside deep learning models, there exist basic computational units called neurons, which function similarly to those in the human brain. Neurons detect small features within an image—such as the shape of an ear, a specific color, or an outline—and compute a value (signal) that is transmitted to the next layer.
In contrast, a circuit refers to a structure in which multiple neurons are connected to jointly recognize a single meaning (concept). For example, to recognize the concept of cat ear, neurons detecting outline shapes, neurons detecting triangular forms, and neurons detecting fur-color patterns must activate in sequence, forming a functional unit (circuit).
Up until now, most explanation techniques have taken a neuron-centric approach based on the idea that “a specific neuron detects a specific concept.” However, in reality, deep learning models form concepts through cooperative circuit structures involving many neurons. Based on this observation, the KAIST research team proposed a technique that expands the unit of concept representation from “neuron → circuit.”
The research team’s newly developed technology, Granular Concept Circuits (GCC), is a novel method that analyzes and visualizes how an image-classification model internally forms concepts at the circuit level.
GCC automatically traces circuits by computing Neuron Sensitivity and Semantic Flow. Neuron Sensitivity indicates how strongly a neuron responds to a particular feature, while Semantic Flow measures how strongly that feature is passed on to the next concept. Using these metrics, the system can visualize, step-by-step, how basic features such as color and texture are assembled into higher-level concepts.
The team conducted experiments in which specific circuits were temporarily disabled (ablation). As a result, when the circuit responsible for a concept was deactivated, the AI’s predictions actually changed.
In other words, the experiment directly demonstrated that the corresponding circuit indeed performs the function of recognizing that concept.
This study is regarded as the first to reveal, at a fine-grained circuit level, the actual structural process by which concepts are formed inside complex deep learning models. Through this, the research suggests practical applicability across the entire explainable AI (XAI) domain—including strengthening transparency in AI decision-making, analyzing the causes of misclassification, detecting bias, improving model debugging and architecture, and enhancing safety and accountability.
The research team stated, “This technology shows the concept structures that AI forms internally in a way that humans can understand,” adding that “this study provides a scientific starting point for researching how AI thinks.”
Professor Jaesik Choi emphasized, “Unlike previous approaches that simplified complex models for explanation, this is the first approach to precisely interpret the model’s interior at the level of fine-grained circuits,” and added, “We demonstrated that the concepts learned by AI can be automatically traced and visualized.”
< Overview of the Conceptual Circuit Proposed by the Research Team >
This study, with Ph.D. candidates Dahee Kwon and Sehyun Lee from KAIST Kim Jaechul Graduate School of AI as co–first authors, was presented on October 21 at the International Conference on Computer Vision (ICCV).
Paper title: Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations
Paper link: https://openaccess.thecvf.com/content/ICCV2025/papers/Kwon_Granular_Concept_Circuits_Toward_a_Fine-Grained_Circuit_Discovery_for_Concept_ICCV_2025_paper.pdf
This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP) under the “Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation” project, the AI Research Hub Project, and the KAIST AI Graduate School Program, and was carried out with support from the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD) at the KAIST Center for Applied Research in Artificial Intelligence.
2025.11.26 View 2124 -
KAIST to Usher in an Era of Nationwide Science Culture: KSOP, OPEN KAIST, and AI Academy
< 2025 OPEN KAIST (Demonstration of the cluster systems and AI drone program conducted in Prof. Il-Chul Moon’s Lab, Department of Industrial & Systems Engineering)>
KAIST announced on November 25th that it is operating the 'Science Education Sharing (KSOP),' 'OPEN KAIST,' and 'KAIST-style IT/AI Academy for the General Public, social contribution programs based on science popularization,in line with the government's policy to spread science culture. Through these initiatives, KAIST is nurturing future science and technology talent and contributing to the popularization of science culture.
KAIST President Kwang Hyung Lee stated, “Under the mission of 'a university that contributes to humanity and society through science and technology,' KAIST is creating a ladder of opportunity through education sharing,” adding, “KSOP and OPEN KAIST are core KAIST programs that help all children dream of becoming scientists, regardless of their economic or regional circumstances. KAIST will continue to actively communicate with the general public and contribute to strengthening national competitiveness in science and technology by pursuing warm science, inclusive education, and a sustainable science culture ecosystem that goes beyond cutting-edge science and technology.”
■ KSOP for Science-Gifted Students from Underprivileged Backgrounds: 8,000 Beneficiaries in 10 Years, 70% Enrollment in STEM Fields
KSOP, operated by the Science Gifted Education Research Institute, is a representative science-sharing program. It selects students with potential in mathematics and science from socially disadvantaged youth and provides direct mentoring by current KAIST undergraduate and graduate students.
Starting with 250 students in 2015, the program expanded to approximately 1,000 participants annually starting in 2022, with a cumulative total of about 8,000 participants by 2025. It has achieved tangible results, with over 70% of graduates advancing into STEM fields, and a knowledge circulation structure has become established where graduates return as mentors.
Creative science education volunteer work has been conducted in underserved areas such as Jeju, Mokpo, and Andong, in addition to Daejeon, Sejong, and Hwaseong, contributing to the alleviation of educational disparities between regions. In particular, the program where mentees teach elementary school students has been cited as a prime example of KAIST's science culture diffusion.
One KSOP graduate who advanced to KAIST and is now been a mentor for five years shared, “Through mentoring, I feel the true value of sharing and service, as well as an inexpressible sense of pride and accomplishment.”
Furthermore, family-unit programs, including parent information sessions, family camps, and counseling support, have strengthened students' emotional and career support. In 2025, the fifth family camp was held, further broadening participation.
'KSOP FRIENDS,' centered on graduates and mentors, has established a virtuous cycle ecosystem connecting scholarships, mentoring, and donations. This initiative has expanded and developed into the 'Daddy-Long-Legs Project,' a representative small-sum regular donation program in which the public can participate.
< KSOP Jeju Island Educational Volunteer Group Photo >
< KSOP Scholarship Award >
■ ‘OPEN KAIST 2025’ to Meet KAIST Laboratories: Record-Breaking Number of Visitors
OPEN KAIST, KAIST's flagship science culture event that opens laboratories and the campus to the public every two years, recorded its highest ever attendance in 2025, with the number of visitors increasing more than fourfold compared to 2023. In particular, the lab tours garnered high interest, with long waiting lists for pre-registration. An elementary school participant commented, “The earthquake research lab tour was so fascinating and very helpful for answering my questions.” Recognizing that some participation was difficult due to the larger-than-expected number of visitors, KAIST announced plans to expand participation opportunities and improve operations in the future.
■ Cultivating Digital Talent through Short-Term Non-Degree IT/Semiconductor Courses for the General Public
The 'KAIST IT Academy' for military personnel is a non-degree program that provides practical, basic training in AI, computer science, and programming, involving KAIST graduate students as instructors. Operated both online and offline, approximately 1,000 trainees participate annually.
The 'SW Academy (Jungle),' a KAIST non-degree software education course, has become a successful model for nurturing young SW talent, with a cumulative 308 people completing the course between 2021 and 2024. Major employers include Naver, Krafton, Team Sparta, Nearthlab, and Woowa Brothers. Jungle trains developers who can be immediately deployed in practical work through hands-on programming education, mentoring by active developers, and planning/design feedback.
Based on the excellence of the Jungle program, Krafton launched and has been operating 'Krafton Jungle' since 2022. This is a social contribution activity by a company founded by KAIST alumnus Chairman Byung-Gyu Jang and is regarded as a prime example of KAIST's talent nurturing model spreading to the private sector.
Furthermore, the KAIST IDEC (IC Design Education Center) trains 240 young people annually as semiconductor design experts through the nurturing of semiconductor design talent, facilitating their entry into the industry.
■ Strengthening National Competitiveness by Building a Future Talent Ecosystem
KSOP received international recognition for its excellence in 2024 by winning the Best Program Award and Best Researcher Award at the Asia-Pacific Conference on Giftedness (APCG).
KAIST is further expanding its future talent platform by launching 'Junior KAIST' in 2025, a science, mathematics, and AI exploration program for youth. KAIST plans to continue strengthening its role as a public research university that grows with the nation through science and technology-based social contribution and the nurturing of future talent.
2025.11.25 View 1543