Course Information
| Session |
|---|
| Credits | 1.5 CEUs or 15 PDHs |
|---|---|
| Registration dates | We accept registrations through the first week of classes, unless enrollment is full, and unless the class was canceled before it started due to low enrollment. |
Introduction to AI and Metadata: Uses with Library Data
$250.00
Dates: September 7 - October 4January 4 - January 31
Credits: 1.5 CEUs or 15 PDHs
Overview: Embark on a comprehensive 4-week journey into the transformative world of artificial intelligence (AI) and its intersection with metadata in library environments. This course delves into the multifaceted opportunities and challenges presented by AI in library data management, offering a balanced and practical perspective. Key topics include the implementation of AI processes, advocacy for data work, and crucial considerations of ethics and copyright.
Participants will develop a strong foundation in AI fundamentals and concepts, understand the pivotal role of metadata in libraries, and explore the intersection of AI and metadata. The course will examine the benefits and challenges of implementing AI in library data management, including data quality, privacy, and security. Students will engage with readings, case studies, discussions, and hands-on applications, fostering robust skills in analyzing and remediating metadata using AI. Additionally, participants will explore advanced tools for creating metadata with AI, ensuring compliance with data standards and best practices.
Learning Outcomes:
By the end of this course, participants will:
- Gain a comprehensive understanding of AI and its applications in metadata and library data.
- Identify and navigate ethical and copyright issues, understanding the broader implications of leveraging AI in library data.
- Develop practical skills in analyzing and remediating metadata using AI tools.
- Learn effective techniques for creating metadata with AI, applicable to both MARC and Dublin Core (in XML) formats.
- Plan and execute AI implementation projects that support data management practices without compromising quality.
Join us for this insightful and practical course, where you will explore the dynamic interplay between AI and metadata, and emerge equipped to harness the potential of AI in enhancing your library’s data management practices.
Participants will develop a strong foundation in AI fundamentals and concepts, understand the pivotal role of metadata in libraries, and explore the intersection of AI and metadata. The course will examine the benefits and challenges of implementing AI in library data management, including data quality, privacy, and security. Students will engage with readings, case studies, discussions, and hands-on applications, fostering robust skills in analyzing and remediating metadata using AI. Additionally, participants will explore advanced tools for creating metadata with AI, ensuring compliance with data standards and best practices.
Course Description
Overview: Embark on a comprehensive 4-week journey into the transformative world of artificial intelligence (AI) and its intersection with metadata in library environments. This course delves into the multifaceted opportunities and challenges presented by AI in library data management, offering a balanced and practical perspective. Key topics include the implementation of AI processes, advocacy for data work, and crucial considerations of ethics and copyright.
Participants will develop a strong foundation in AI fundamentals and concepts, understand the pivotal role of metadata in libraries, and explore the intersection of AI and metadata. The course will examine the benefits and challenges of implementing AI in library data management, including data quality, privacy, and security. Students will engage with readings, case studies, discussions, and hands-on applications, fostering robust skills in analyzing and remediating metadata using AI. Additionally, participants will explore advanced tools for creating metadata with AI, ensuring compliance with data standards and best practices.
Learning Outcomes:
By the end of this course, participants will:
- Gain a comprehensive understanding of AI and its applications in metadata and library data.
- Identify and navigate ethical and copyright issues, understanding the broader implications of leveraging AI in library data.
- Develop practical skills in analyzing and remediating metadata using AI tools.
- Learn effective techniques for creating metadata with AI, applicable to both MARC and Dublin Core (in XML) formats.
- Plan and execute AI implementation projects that support data management practices without compromising quality.
Join us for this insightful and practical course, where you will explore the dynamic interplay between AI and metadata, and emerge equipped to harness the potential of AI in enhancing your library’s data management practices.
Participants will develop a strong foundation in AI fundamentals and concepts, understand the pivotal role of metadata in libraries, and explore the intersection of AI and metadata. The course will examine the benefits and challenges of implementing AI in library data management, including data quality, privacy, and security. Students will engage with readings, case studies, discussions, and hands-on applications, fostering robust skills in analyzing and remediating metadata using AI. Additionally, participants will explore advanced tools for creating metadata with AI, ensuring compliance with data standards and best practices.
Robin Fay
Robin Fay is an experienced Cataloging and Metadata Librarian, trainer, and author working where metadata, cataloging, systems, and AI intersect. She has led cataloging, migration, and data quality projects across the GLAM landscape — federal agencies, multistate academic consortia including the Orbis Cascade Alliance and the University System of Georgia, and academic, community college, public library, and museum collections. Her practice spans MARC to BIBFRAME, system migrations, authority and entity management, controlled vocabularies, and linked data, including where AI tools help in metadata work and how to build human-AI collaborations for our work. She is the author of Semantic Web Technologies and Social Searching for Librarians and actively writes at linkedin and elsewhere. A frequent guest on WREK's Lost in the Stacks, she discusses metadata and semantic web topics through the lens of practical application. Connect with Robin via her linktr.ee/robinfay or at robinfay.net
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