The Digital Public Library of America (DPLA) has awarded a grant to University Libraries for a project exploring how artificial intelligence (AI) can be used responsibly to support cultural heritage stewardship while engaging students in experiential learning.
The Metadata Remediation and AI-Enhanced Workflow Pilot was selected under DPLA's AI innovation focus area and will develop practical, lightweight, scalable, and adaptable workflows that support metadata remediation and aggregation readiness. This work also prepares collections for the Digital Virginias service hub, which connects cultural heritage institutions across the region to DPLA’s national aggregation platform.
Centering people over automation
The project investigates how tools such as GPT and OpenRefine can meaningfully assist with metadata remediation across diverse digital collections. Unlike many AI projects that focus on automation, this work keeps people, not automated systems, at the center.
“Many AI projects focus on highly experimental or fully automated pipelines, but this work intentionally centers human expertise,” said Wen Nie Ng, digital collections and emerging formats librarian. “Instead of replacing catalogers or metadata professionals, it looks at how AI can help reduce repetitive tasks and elevate the intellectual labor that humans do best — making contextual, ethical, and community-informed decisions about collections.”
The project asks a critical question for the future of libraries and archives. How can cultural heritage professionals prepare for a world where AI is ubiquitous, yet still requires thoughtful human oversight?
Hands-on student learning
A key element of the pilot is student engagement that goes beyond technical exposure to emphasize professional judgment and critical thinking. Library student employees work directly with real metadata sets, performing tasks such as subject enrichment, vocabulary mapping, and evaluating description fields across collections. Through this hands-on work with AI-assisted workflows, students learn how to evaluate, question, and contextualize automated outputs. The goal goes beyond teaching students how to use AI tools to helping them understand their limits and the responsibility that comes with applying them in cultural heritage work.
“Students learn not only how to use AI tools, but also when and why not to use them,” said Ng. “The training is intentional. It builds professional judgement by asking students to analyze AI-generated outputs, identify inconsistencies, question assumptions, and understand the limitations of automated systems. Many people say that AI disrupts students’ critical thinking, but in our experience, the opposite is true when AI is taught as a tool rather than as an authority. When students recognize what AI cannot do, they naturally begin to question, reflect, and reason more deeply.”
Ng used an analogy to explain the project’s teaching philosophy.
“A spoon helps you eat, but it doesn’t teach you how to use it,” said Ng. “You still need to learn the angle, grip, and movement yourself. Across cultures, tools are used differently. In many Western contexts, a spoon is for soups or soft foods. In Southeast Asia, it is essential for scooping rice. The tool is the same, but the technique comes only through human learning and experience. AI functions the same way. It can assist with part of the process, but it cannot replace the ability to interpret context, apply standards, make ethical choices, or understand cultural nuance. These responsibilities still rest with people.”
Reflecting real-world digital curation
The workflow developed through this pilot mirrors the realities of digital curation work: the balance between adopting new technologies and maintaining accountability.
“We use tools, but we also scrutinize them,” Ng said. “It’s not a one-size-fits-all, and each collection requires attention to its community, purpose, and context.”
By documenting lightweight steps, ethical considerations, and decision points, the pilot aims to support not only major institutions but also small museums, community archives, and under-resourced organizations that may lack specialized staffing.
A replicable workflow with broad impact
One of the project’s major outcomes will be a reusable, AI-assisted metadata workflow designed to be lightweight and scalable, with small and under-resourced institutions in mind. Through transparent documentation and clearly defined human oversight, the workflow supports community archives and smaller organizations that may lack specialized staffing or advanced technical infrastructure. This approach helps reduce barriers to metadata remediation while enabling more collections to be responsibly prepared for aggregation and discovery.
“Like the spoon analogy, the workflow provides a shared structure, but each community can adapt it based on their unique collections, staffing, and values,” said Ng.
This adaptability is key to preparing collections for aggregation within DPLA’s national digital ecosystem, helping more stories and community histories become visible and accessible.
Positioning Virginia Tech as a leader
The project offers University Libraries both immediate and long-term value. AI-supported metadata remediation will reduce processing bottlenecks and strengthen readiness for the Digital Virginias service hub. Broadly, the project positions Virginia Tech as a leader in responsible, community-oriented AI experimentation in libraries.
“For communities across Virginia and Appalachia, the project lowers barriers to sharing local histories, ensuring that more voices and stories can be represented in national digital ecosystems,” said Ng.
Ng’s personal motivation for the project stems from firsthand experience with the challenges of metadata remediation.
“I am passionate about this work because I have seen how much time and labor metadata remediation requires,” said Ng. “It is foundational to creating access to materials, yet often overwhelming for institutions with limited staffing and resources. AI shouldn’t replace expertise, but it can support it, creating more room for meaningful human work. My hope is that this project becomes an example of how AI can be used with care, accountability, and respect for cultural context, while strengthening the skills of emerging professionals.”
