Information Technology Management Evaluating Real-Time AI Translation in Japan–US Virtual Class Collaboration: Participation Equity and Learning Efficiency International class collaborations (ICCs) and virtual exchange can expand intercultural learning and collaboration, but unequal proficiency in a shared language often produces unequal participation. This study examines whether AI-enabled live translation can reduce those barriers in Japan–U.S. virtual class collaboration involving Japanese undergraduates with limited English proficiency and U.S. graduate students with no Japanese ability. Using mixed methods, quasi-experimental field design, the study evaluates Microsoft Teams live translated captions across three common ICC activity types: teaching/instruction, presentation, and discussion. It also includes pilot comparisons with Sokuji, a speech-to-speech translation system, to explore modality-specific trade-offs between written captions and translated audio. Data sources include post-session Likert-scale questionnaires, open-ended responses, structured observation of speaking turns and clarification requests, and follow-up interviews. The study focuses on two primary outcomes: perceived learning efficiency and participation equity. Preliminary pilot observations suggest that caption-based translation can support comprehension but may increase visual and cognitive demands, whereas audio translation may improve immersion and note-taking while introducing timing and workflow challenges. The project contributes classroom- grounded evidence for selecting translation modalities, structuring multilingual online interaction, and designing more equitable international collaboration. Presentation Type: Oral Presentation (May 20, 9:30am–5:00pm) Keywords: virtual exchange, multilingual online collaboration, real-time AI translation, participation Aizhan Manatkyzy*; Hideki Takei, DBA Project Mentor(s): Hideki Takei, DBA Data Governance and Security: Techniques for Discovering, Classifying, and Protecting Sensitive Data McKenzie Manley, Elvin Cruz-Contreras, Bo Dods, Sam Hermenet, Andrew Kryvenko, Ayden Mooney Project Mentor(s): Upakar Bhatta, PhD In a world where technology is improving exponentially, the expansion of data gathering and usage practices is matching this growth. As sensitive information is gathered, companies and organizations need to ensure their data is kept secure from bad actors wishing to exploit it. The concept of data governance covers the techniques used to discover data, classify and label it, and protect it from exploitation. This presentation examines these concepts and the best practices in its implementation. There are many different strategies and tools that can be used to complete these objectives. From data exploration and visualization in the discovery stage to the use of encryption and audit logging in protection, the options are expansive. It is also critical to consider the various data governance frameworks in the usage of these techniques. In the world of data, official guidelines like the ISO 29100 and the General Data Protection Regulation (GDPR) must be upheld. Utilizing data governance frameworks in the gathering, storage, and usage of data helps ensure adherence to these standards. Understanding the concepts of data governance provides strategic and technical guidance to organizations, and helps align business, legal, and security teams towards the goal of data security. Presentation Type: Oral Presentation (May 20, 9:30am–5:00pm) Keywords: Data Governance, Data Security, Cybersecurity, Data Classification SOURCE Form ID: 60 equity, learning efficiency SOURCE Form ID: 252
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