AI for Restoration & Preservation

Objective: Explore how AI is transforming conservation practices and how to teach these methods.
Key Points:

• Using AI for color reconstruction and digital restoration of damaged works.

• AI imaging to detect underpaintings, hidden text, or forensics of forgery.

• Digitizing fragile works with higher fidelity through ML-driven upscaling.

• Case studies: restoration of Renaissance paintings, photographic archives.

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Demo/Exercise: Walkthrough of an AI tool reconstructing a faded manuscript or damaged artwork.
Potential Guests: Conservation scientists from major museums, universities.
Takeaway: Students learn how AI supports heritage preservation and cultural continuity.

Teaching Authenticity & Provenance in the AI Era

Objective: Equip students to critically evaluate authenticity of artworks.
Key Points:

• Spotting AI-generated fakes in photography, painting, and digital art.

• Provenance tracking with blockchain and watermarking.

• Classroom debates: when is authenticity about the process vs. the result?

• Case study: Christie’s AI auction controversy.

Exercise: Students analyze 10 images (mixed human + AI) and defend authenticity judgments.
Takeaway: Students develop visual literacy to distinguish authenticity.

Curation with AI: Algorithms as Co-Curators

Objective: Show how AI tools can assist curators and challenge traditional methods.
Key Points:

• AI-driven exhibition design: clustering artworks by theme or visual similarity.

• Generating interactive labels, AR guides, and educational content for visitors.

• Risks of algorithmic bias in curation (Western-centric datasets, exclusion).

Demo: Faculty demo of an AI clustering tool that re-organizes a digital collection.
Potential Speakers: Museum curators + technologists in AI UX.
Takeaway: Students gain a vision of how AI changes not just art but how art is presented.

Digital Exhibitions & Accessibility

Objective: Highlight how AI expands access to museum and art history education.
Key Points:

• Building immersive VR/AR exhibitions with AI-generated reconstructions.

• AI translation and captioning for multilingual/global visitors.

• Accessibility: narrating artworks for visually impaired audiences.

• Digital-first museums: preserving collections online and in the metaverse.

Exercise: Students design a small online AI-assisted exhibition as a group project.
Takeaway: Students see how AI expands audiences and democratizes access.