University Libraries Collaborative Research Grant

The Collaborative Research Grant Program Awards support collaborative research across Virginia Tech.

AI Initiative

This AI initiative offers courses to help professionals effectively use AI tools.

Mission Statement

The mission of the CDRS's AI initiatives is to empower innovation, collaboration, and ethical integration of artificial intelligence within library operations, services, and research. By fostering an AI-driven culture, we aim to enhance library workflows, support interdisciplinary research, and provide training opportunities that build AI literacy among staff, faculty, and students. Through partnerships across campus and with external stakeholders, we are committed to advancing practical applications of AI in libraries while championing the principles of privacy, equity, and accessibility.

Vision Statement

Our vision is to position the CDRS as a leader in leveraging artificial intelligence to transform academic library operations, services, and research. We aspire to create a dynamic environment where AI enhances library workflows, optimizes user experiences, and drives interdisciplinary collaboration to address real-world challenges. We aim to become a model for other university libraries by demonstrating how AI can be ethically and effectively applied in both library contexts and academic research. Through innovative projects, strategic partnerships, and support for faculty and student research, we envision a future where libraries play a pivotal role in advancing responsible AI practices across academia.

Ongoing and Continuous Efforts

• Workshops and Training Programs: Conducting workshops in the library to build AI literacy among staff, faculty, and students, empowering them to use AI tools effectively and ethically.
• Fostering an AI-Driven Culture: Promoting the integration of AI into library operations to enhance workflows, improve services, and support innovation.
• Interdisciplinary Collaboration: Partnering with departments across campus to foster interdisciplinary research and explore funding opportunities through agencies such as IMLS and NSF.
• Cross-Campus Partnerships: Engaging with diverse stakeholders across campus to strengthen collaboration and expand the impact of AI-related initiatives.
• Service Innovation: Identifying and implementing AI-driven opportunities to optimize existing or new library services.
• Seed Funding for AI Research: Providing seed funding through programs like Collaborative Research Grants to support faculty, researchers, and students in developing AI-based solutions within library settings.
• AI Project Management: Leading library-centered AI projects to ensure their successful implementation while facilitating seamless integration of applications across campus systems.
• Ethical AI Advocacy: Championing ethical, transparent, and responsible uses of AI in library settings with a strong focus on privacy, equity, and accessibility.

Core Research Questions

How can large-scale, complex collections be organized, retrieved, and synthesized to advance knowledge, transparency, and reuse?

More specifically:

• How can long-form scholarly texts be segmented, classified, and represented to improve access and discovery?
• What retrieval models best connect user queries with complex, interdisciplinary content?
• How can retrieval-augmented language models generate structured, source-grounded synthesis from large corpora?
• What computational approaches address incomplete or inconsistent metadata to support interoperability and preservation?
• How should AI-driven methods be evaluated for reproducibility, transparency, and long-term integration into scholarly systems?
• How can computational methods extend systematic review and evidence synthesis to large-scale scholarly corpora, enabling cumulative insights from heterogeneous sources?

Themes of Investigation

• Document Structure and Classification

  Algorithms for partitioning dissertations and other scholarly works into chapters, sections, figures, and tables, and assigning meaningful roles to these components.

• Retrieval and Ranking

  Hybrid sparse–dense retrieval models that connect diverse user queries to relevant scholarly content at scale.

• Knowledge Synthesis

  Retrieval-augmented generation pipelines that integrate context from large corpora to produce grounded summaries of research contributions.

• Metadata and Data Quality

  Computational strategies for extracting, validating, and enriching metadata across heterogeneous records.

• Evidence Synthesis

  Computational methods for scaling and accelerating evidence synthesis by integrating natural language processing and information retrieval with established review practices. In collaboration with the Libraries’ evidence synthesis service, these approaches combine disciplinary expertise with computational analysis to increase the speed, scale, and sophistication of synthesis projects across the university.

• AI for Scholarly Knowledge Systems

  Models that combine classification, retrieval, and synthesis into reproducible and transparent infrastructures for scholarly communication.