Conference Agenda (All times are shown in Eastern Daylight Time)

This study evaluates the usability of four metaverse libraries—Chilgok Public Library, Daegu Integrated Public Library, Community Virtual Library, and Caledon Library—using a multi-method approach. A task-based evaluation based on Nielsen’s usability criteria was combined with the System Usability Scale (SUS), AttrakDiff, and Photovoice to capture users’ cognitive, behavioral, and emotional experiences. Results show that unfamiliarity with virtual environments posed initial challenges, particularly in Second Life-based libraries. However, users’ efficiency improved with repeated tasks, and participants without prior metaverse experience reported higher emotional satisfaction. Photovoice data revealed immersive visuals alongside usability issues such as navigation difficulties and unclear interactions. The study highlights the need for platform-specific design improvements and usability training. It contributes to metaverse library research by offering comprehensive evaluation methods and suggesting directions for designing more user-centered metaverse library experiences.

The purpose of this study is to explore users' expectations of LLMs, examine their confirmation of perceived system performance, and examine how these factors influence their overall satisfaction with the system. We analyzed data collected from LLM users through an online survey using Welch’s ANOVA and regression analysis. The findings demonstrate that users’ expectations and confirmation of LLMs are fluid across different socio-cultural variables, spanning age, gender, and educational levels. Additionally, users’ perceived system performance, of LLMS, significantly influences their confirmation of the system. Specifically, both perceived usefulness and perceived ease of use have a statistically significant effect on confirmation. Both of our sub-models demonstrate that perceived system performance influences users' confirmation of a given system, and users’ confirmation is a strong determinant of their satisfaction. Furthermore, our results indicate an uneven distribution and penetration of AI technologies with respect to age, gender and educational level.

This study explores how the National Central Library in Taiwan leverages a big data service platform to empower public libraries, enabling them to enhance reader participation through data-driven decisions. The platform systematically processes de-identified circulation data through comprehensive cleaning, integration, and standardization procedures, enabling libraries to gain actionable insights through advanced visualization tools. This data empowerment initiative reflects the evolution of Taiwan's libraries towards Library 4.0, where analytics capabilities enable libraries to transform from passive information providers to active service innovators, providing personalized, differentiated reader experiences. The platform strengthens public libraries' capacity to promote reading engagement by providing detailed metrics and trend reports, facilitating evidence-based policy making and service improvements. Through continuous data analysis and visualization, libraries can better understand their communities' reading preferences and adapt their services accordingly. In addition to basic statistical analysis, the platform incorporates and develops data mining techniques to analyze reading behaviors across demographics, offering deeper insights into reader interests and preferences. Furthermore, the study investigates the application of these data mining techniques to facilitate reader resource recommendations, ultimately enhancing library services and promoting a more engaged reading community.