Hwajung+Hong
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Answering Calls for Help Even at Dawn: an AI TA makes a Successful Debut at KAIST
- Research team of Professor Yoonjae Choi of Kim Jaechul Graduate School of AI and Professor Hwajung Hong of the Department of Industrial Design, development of AI assistant (VTA) that helps with operation and learning in lectures for 477 students
- Responds to students’ questions about theory and practice 24 hours a day by referring to class slides, coding practice materials, and lecture videos
- Releases the system’s source code to support the development of customized learning assistance systems and application in educational settings in the future
< Photo 1. (From left) PhD candidate Sunjun Kweon, Master's candidate Sooyohn Nam, PhD candidate Hyunseung Lim, Professor Hwajung Hong, Professor Yoonjae Choi >
“At first, I didn’t have high expectations for AI assistant (VTA), but it was very useful because I could get immediate answers when I suddenly asked questions about concepts that I was curious about late at night,” he said. “In particular, I was able to ask questions about parts that I hesitated to ask a human assistant without feeling burdened, and as I asked more questions, my understanding of the class increased.” (KAIST Ph.D. student Ji-won Yang)
KAIST (President Kwang-Hyung Lee) announced that a joint research team of Professor Yoonjae Choi of Kim Jaechul Graduate School of AI and Professor Hwajung Hong of Industrial Design Department On the June 5th, it was announced that it had developed a ‘Virtual Teaching Assistant (VTA)’ that can provide personalized feedback to each student even in large lectures and successfully applied it to actual lectures.
This study is the first domestic case in which VTA was introduced to the ‘Programming for Artificial Intelligence’ course of the Kim Jaechul Graduate School of AI, which 477 master’s and doctoral students took in the fall semester of 2024, and its effectiveness and practicality were verified on a large scale in an actual educational setting.
The AI teaching assistant developed in this study is an agent specialized for classes, different from general chatGPT or existing chatbots. The research team automatically vectorized a large amount of class materials such as lecture slides, coding practice materials, and lecture videos, and implemented a Retrieval Augmented Generation (RAG) structure in which questions and answers are answered based on this.
< Photo 2. Students demonstrating how the Virtual Teaching Assistant works >
When a student asks a question, the system searches for the most relevant class materials in real time based on the context of the question and generates a response. This process is not simply calling a large language model (LLM), but is designed as a data-based question-and-answer that corresponds to the class content, so it can be said to be an intelligent system that secures both learning reliability and accuracy.
The first author of this study and the responsible teaching assistant for the class, PhD candidate Sunjun Kweon, said, “In the past, there were many repetitive and basic questions such as content already explained in class or simple concept definitions, so it was difficult for teaching assistants to focus on key questions.” He continued, “After the introduction of VTA, students reduced repetitive questions and focused on essential questions, so the burden on teaching assistants was noticeably reduced and they were able to focus on more high-level learning support.”
In fact, the number of questions that teaching assistants had to answer directly decreased by about 40% compared to last year’s class.
< Photo 3. A student working with VTA. >
More than half of all students actually used VTA during the 14-week operation, and a total of 3,869 questions and answers were recorded. In particular, the frequency of VTA use was higher for students who were not majoring in AI or lacked prior knowledge, which suggests that VTA provided practical help as a learning aid.
In addition, the analysis results showed that students tended to ask questions about theoretical concepts to VTA more often than to human assistants. This can be interpreted as the AI assistant providing an environment where students can freely ask questions without being evaluated or feeling uncomfortable, thereby actively encouraging learning participation.
As a result of the survey conducted three times before, during, and after class, students reported higher reliability, response appropriateness, and comfort with VTA than at the beginning. In particular, students who had experience of hesitating to ask questions to human assistants showed higher satisfaction with their interactions with AI assistants.
< Figure 1. Internal structure of the AI Teaching Assistant (VTA) applied in this course. It follows a Retrieval-Augmented Generation (RAG) structure that builds a vector database from course materials (PDFs, recorded lectures, coding practice materials, etc.), searches for relevant documents based on student questions and conversation history, and then generates responses based on them. >
Professor Yoonjae Choi, who led the research and is the professor in charge of the class, said, “The significance of the study lies in the fact that it has confirmed that AI technology can provide practical help to both students and instructors. We hope that this technology will be expanded to more diverse classes in the future.”
The research team is supporting other educational institutions and researchers to develop customized learning assistance systems based on this by releasing the system's source code on the developer platform GitHub and apply it to educational settings.
< Figure 2. Initial screen of the AI Teaching Assistant (VTA) introduced in the "Programming for AI" course. It asks for student ID input along with simple guidelines, a mechanism to ensure that only registered students can use it, blocking indiscriminate external access and ensuring limited use based on students. >
The related paper was accepted by the 'ACL 2025 Industry Track', one of the most prestigious international academic conferences in the field of natural language processing (NLP), on May 9, 2025, and its research excellence was recognized.
※ Paper title: A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant
< Figure 3. Example conversation with the AI Teaching Assistant (VTA). When a student inputs a class-related question, the system internally searches for relevant class materials and then generates an answer based on them. In this way, VTA provides learning support by reflecting class content in context. >
Meanwhile, this study was conducted with the support of the KAIST Center for Teaching and Learning Innovation, the National Research Foundation of Korea, and the National IT Industry Promotion Agency.
2025.06.05 View 104 -
“For the First Time, We Shared a Meaningful Exchange”: KAIST Develops an AI App for Parents and Minimally Verbal Autistic Children Connect
• KAIST team up with NAVER AI Lab and Dodakim Child Development Center Develop ‘AAcessTalk’, an AI-driven Communication Tool bridging the gap Between Children with Autism and their Parents
• The project earned the prestigious Best Paper Award at the ACM CHI 2025, the Premier International Conference in Human-Computer Interaction
• Families share heartwarming stories of breakthrough communication and newfound understanding.
< Photo 1. (From left) Professor Hwajung Hong and Doctoral candidate Dasom Choi of the Department of Industrial Design with SoHyun Park and Young-Ho Kim of Naver Cloud AI Lab >
For many families of minimally verbal autistic (MVA) children, communication often feels like an uphill battle. But now, thanks to a new AI-powered app developed by researchers at KAIST in collaboration with NAVER AI Lab and Dodakim Child Development Center, parents are finally experiencing moments of genuine connection with their children.
On the 16th, the KAIST (President Kwang Hyung Lee) research team, led by Professor Hwajung Hong of the Department of Industrial Design, announced the development of ‘AAcessTalk,’ an artificial intelligence (AI)-based communication tool that enables genuine communication between children with autism and their parents.
This research was recognized for its human-centered AI approach and received international attention, earning the Best Paper Award at the ACM CHI 2025*, an international conference held in Yokohama, Japan.*ACM CHI (ACM Conference on Human Factors in Computing Systems) 2025: One of the world's most prestigious academic conference in the field of Human-Computer Interaction (HCI).
This year, approximately 1,200 papers were selected out of about 5,000 submissions, with the Best Paper Award given to only the top 1%. The conference, which drew over 5,000 researchers, was the largest in its history, reflecting the growing interest in ‘Human-AI Interaction.’
Called AACessTalk, the app offers personalized vocabulary cards tailored to each child’s interests and context, while guiding parents through conversations with customized prompts. This creates a space where children’s voices can finally be heard—and where parents and children can connect on a deeper level.
Traditional augmentative and alternative communication (AAC) tools have relied heavily on fixed card systems that often fail to capture the subtle emotions and shifting interests of children with autism. AACessTalk breaks new ground by integrating AI technology that adapts in real time to the child’s mood and environment.
< Figure. Schematics of AACessTalk system. It provides personalized vocabulary cards for children with autism and context-based conversation guides for parents to focus on practical communication. Large ‘Turn Pass Button’ is placed at the child’s side to allow the child to lead the conversation. >
Among its standout features is a large ‘Turn Pass Button’ that gives children control over when to start or end conversations—allowing them to lead with agency. Another feature, the “What about Mom/Dad?” button, encourages children to ask about their parents’ thoughts, fostering mutual engagement in dialogue, something many children had never done before.
One parent shared, “For the first time, we shared a meaningful exchange.” Such stories were common among the 11 families who participated in a two-week pilot study, where children used the app to take more initiative in conversations and parents discovered new layers of their children’s language abilities.
Parents also reported moments of surprise and joy when their children used unexpected words or took the lead in conversations, breaking free from repetitive patterns. “I was amazed when my child used a word I hadn’t heard before. It helped me understand them in a whole new way,” recalled one caregiver.
Professor Hwajung Hong, who led the research at KAIST’s Department of Industrial Design, emphasized the importance of empowering children to express their own voices. “This study shows that AI can be more than a communication aid—it can be a bridge to genuine connection and understanding within families,” she said.
Looking ahead, the team plans to refine and expand human-centered AI technologies that honor neurodiversity, with a focus on bringing practical solutions to socially vulnerable groups and enriching user experiences.
This research is the result of KAIST Department of Industrial Design doctoral student Dasom Choi's internship at NAVER AI Lab.* Thesis Title: AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation* DOI: 10.1145/3706598.3713792* Main Author Information: Dasom Choi (KAIST, NAVER AI Lab, First Author), SoHyun Park (NAVER AI Lab) , Kyungah Lee (Dodakim Child Development Center), Hwajung Hong (KAIST), and Young-Ho Kim (NAVER AI Lab, Corresponding Author)
This research was supported by the NAVER AI Lab internship program and grants from the National Research Foundation of Korea: the Doctoral Student Research Encouragement Grant (NRF-2024S1A5B5A19043580) and the Mid-Career Researcher Support Program for the Development of a Generative AI-Based Augmentative and Alternative Communication System for Autism Spectrum Disorder (RS-2024-00458557).
2025.05.19 View 1421