This project investigates how intergenerational collaboration can enhance older adults' access to and engagement with health and social information in a digital society. Young and older adults collaborate in libraries and community centers to seek, share, and evaluate information using AI-driven tools — with students trained as information intermediaries. The project develops scalable intergenerational literacy programs that promote digital inclusion and healthy aging. This project is currently funded by SKKU (2026), with plans to expand into a larger, long-term research program.
Research Team
Sanghee Oh (PI, SKKU), Junghee Bae (Co-PI, SKKU), Haena Lee (Co-PI, SKKU)
SKKU student researchers: Sine Choi
Work-in-progress studies
Developing a survey-based study to examine perceptions and use of generative AI across different age groups and their associations with social factors and contexts.
This project establishes a FAIR-based data-sharing ecosystem in the biomedical domain by enhancing the findability, accessibility, interoperability, and reusability of research data. It leverages AI and automation to advance machine-actionable data sharing, and designs sustainable governance models that ensure transparency, reproducibility, and ethical data stewardship. The project aims to build an intelligent open-science infrastructure that strengthens Korea's global competitiveness in biomedical data management.
Research Team
Wonsik Shim (PI, SKKU), Youngseek Kim (Co-PI, SKKU), Sanghee Oh (Co-PI, SKKU), Juhee Cho (Co-PI, SKKU)
SKKU student researchers: Yunseo Park, Barun Hwang, Seyun Sim
Work-in-progress studies
Conducting interviews with biomedical and health researchers to examine their data practices, including data management, data sharing, and data reuse.
Selected publications/presentations
Oh, S.*, Park, Y. & Sim, S. (2026) Data availability statements in mega journals: A comparative analysis of global and korea-affiliated publications in health and medical research. Learned Publishing, 39(3), e2083.. https://onlinelibrary.wiley.com/doi/10.1002/leap.2083
Oh, S.*, Park, Y., & Sim, S. (2025). Exploring Data Sharing in Medical and Health Sciences through Mega Journals. Proceedings of the Association for Information Science and Technology, 62(1), 1616-1618.
This three-year project examines information inequality across regions in South Korea through an AI-powered visualization platform grounded in Local Information Landscapes (LIL) theory. It develops predictive models using survey and open library data, and delivers a prototype that enables exploration of regional information gaps. The project aims to inform evidence-based policy-making and strengthen AI and data literacy in local communities.
Research Team
Jongwook Lee (PI, KNU), Sanghee Oh (SKKU), Myeong Yi (GMU), Seungwon Yang (LSU). Kwonho Choi (KNU), Minsook Park (FSU)
SKKU student researchers: Hyunsoo Yoon, Sungha Moon, Daechan Yang, Hojin Park, Chaeri Son
Work-in-progress studies
Conducting interviews with biomedical and health researchers to examine their data practices, including data management, data sharing, and data reuse.
Selected Publications/Presentations
Yoon, H., Moon. S. & Oh, S.* (2026, accepted). Everyday information practices in lifelong learning: Motivations, behaviors, and contextual influences. Journal of Documentation.
Yang, S., Yang, D., Son, C., Park, H., & Oh, S.* (2025). Examining Urban and Rural Information Needs through Topic Modeling: A Case of South Korea. Proceedings of the Association for Information Science and Technology, 62(1), 1144-1148.
Oh, S., Lee, M., & Yi, M.* (2025, accepted). Information awareness, access, and socioeconomic deprivation: a cross-jurisdictional study of local health information. Aslib Journal of Information Management.
Yi, M., Lee, J., Kang, W., & Oh, S. (2023). Aggregate‐Level Analysis of Information Behavior: A Study of Public Library Book Circulation. Proceedings of the Association for Information Science and Technology, 60(1), 1025-1027.
Kang, W., Yi, M., Lee, J., & Oh, S. (2023). AI or Authors?: A Comparative Analysis of BERT and ChatGPT's Keyword Selection in Digital Divide Studies. Proceedings of the Association for Information Science and Technology, 60(1), 1004-1006.