AI Art History Lab
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UChicago Proposal

AI Art History Lab

A next-phase platform for AI-assisted, computational, and ethically governed art history.

From DCADP’s digital reconstruction legacy to a new research paradigm

Click “Next,” press →, or use the dots below.

Starting Point

Three foundations become one platform

DCADP, the Wu Hung Archive, and the East Asian Scroll Painting project already contain the core ingredients: digital assets, archives, research questions, museum partnerships, and public-facing outputs.

Flowchart showing DCADP, Wu Hung Archive, and East Asian Scroll Painting moving through a transitional phase into AI Art History Lab.
Core Argument

Not just more scanning

The next challenge is to build infrastructure for AI-assisted research, computational analysis, ethical data governance, and public interpretation.

1
Research New methods grounded in digital evidence and art-historical judgment.
2
Teaching Training students to combine humanistic interpretation with AI and data literacy.
3
Public Engagement Exhibitions and interfaces that translate scholarship into experience.
Why AI?

AI is already changing scholarship

For art historians, the urgent question is not whether AI will enter the field, but how we shape its use critically, ethically, and productively.

Evidence extractionOCR, metadata, entity recognition, archival search.
Formal comparisonPattern recognition, visual similarity, 3D comparison.
Interpretive systemsPublic-facing interfaces, guided interaction, adaptive storytelling.
Inside the Lab

Three working areas

A
AI Tool Development and Implementation Practical tools for research workflows, metadata, provenance, visual analysis, and internal project management.
B
Interaction Research and Development Migration from exhibition-based interaction to sustainable web-based and museum-facing interfaces.
C
Computational Art History Research Case studies that test 3D comparison, dataset design, AI-assisted interpretation, and theory-building.
Strengths and Gaps

Build from what already works

The Lab consolidates existing capabilities while addressing the next methodological needs.

Current Strengths 3D scanning, digital asset management, cataloging, web development, digital storytelling, digital curation, museum collaboration.
Areas to Strengthen Data processing, computational analysis, computer vision, interaction development, ethical AI frameworks, student training.
Pilot Year

2026–2027 work plan

The first year should demonstrate feasibility, build internal collaboration, and secure funding for 2027–2029.

Research
Yixian Luohan pilot
Test 3D comparison, visual analysis, dataset organization, and AI-assisted interpretation.
Data
Complete DCADP scanning
Europe and Canada, with capture standards, permissions, metadata, and intended uses documented.
Technology
AI Art History Hackathon
Bring together art history, data science, computer science, design, and digital humanities.
Governance
Ethical AI paper
Develop recommendations with interested museum partners.
Long-Term Outcomes

Why this matters

UChicago leadershipPosition Art History and CAEA at the center of AI-assisted digital and computational art history.
Museum partnershipsOffer partners analysis, interpretation, public presentation, and ethical data governance.
Student recruitmentConnect art history with AI literacy, digital curation, and career-facing skills.
DCADP legacyTurn a grant-funded project into the foundation for a durable research paradigm.

DCADP has shown that digital technology can reconnect dispersed cultural heritage. The AI Art History Lab asks how art historians can lead the responsible use of AI in interpreting, teaching, and publicly sharing it.