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LESSONS FROM A NEW AI-ASSISTED DIGITAL COLLECTIONS TOOL

Growing Collections, Improving Access, Evolving Practice

By Rachel Walton , Liriam Tobar, Wenxian Zhang, and Lee Dotson

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Presenters

SFA ANNUAL MEETING - MAY 2026

Archives & Digital Services Coordinator, Rollins College

Liriam Tobar

Head of Archives & Special Collections, Rollins College

Wenxian Zhang

JSTOR Digital Stewardship, Senior Digital Collections Success Manager, ITHAKA

Lee Dotson

Head of Library Digital Strategies, Rollins College

Rachel Walton

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Agenda

Challenges

Tool Demo

Rollins Pilot

Case Studies

Introduction

Tool Assessment

Other

Takeaways

Q&A

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Challenges

Digitization Backlog

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Limited Time, Staff, Resources

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Barrier to Digital Access

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Seeklight

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Purpose

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Allows archivists to process, describe, and manage digital collections more efficiently while also supporting accessibility and discoverability.

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Key Capabilities

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    • Automated metadata creation - Rapidly creates standards-compliant descriptive metadata (based on Dublin Core) for uploaded media files.

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    • Transcription generation - Generates transcripts for text-based materials, including handwritten, typed, and mixed-media documents.

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    • Supports archival standards and workflows - All AI-generated content is tagged as such, allowing staff to review, edit, and validate the records before publishing if needed/desired.

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Tool Demo

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Rollins College Pilot - Case Studies

Photographical Collection

Handwritten Manuscripts

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Typed

Documents

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Photographical Collection

The Bill Loving Collection

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Photographical Collection

The Bill Loving Collection

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More Photographs

Tomokan Yearbook (1926)

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Typed Documents

Board of Trustees Minutes (1905-1924)

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Handwritten Materials

Hamilton Holt Diary (1946)

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Handwritten Materials

Samuel H. Mead, Personal Diary (1863-1864)

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Type of Collection/Materials

Pros

Cons

Photography

Strong visual descriptions

Some review and edit likely needed

Text Documents

Highly accurate transcription

Limiting for manuscripts

Handwritten Materials

Support rapid transcription

Legibility depedant

Assessment

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Assessment

JSTOR Seeklight excels at transcribing printed and legible handwritten documents and performs well with automated image analysis.

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For us, its limitations emerged in the following contexts:

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SFA ANNUAL MEETING - MAY 2026

    • Poor handwriting legibility or image quality
    • Photographs requiring highly/locale-specific metadata descriptors
    • Images with sensitive content

In these scenarios, digital collection content creation is best completed through detailed, manual, human review and professional judgment rather than an automated approach.

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Other Takeaways

Project Summary Feaure

Student Worker Friendly

Improved Search Experience

Platform Recognized by Students

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Questions?

Contact Us!

Rollins College Archives and Special Collections

archives@rollins.edu

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JSTOR Digital Stewardship

lee.dotson@ithaka.org

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