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
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 a Cataloging/Metadata Librarian and Trainer who has worked with academic, public, community college libraries and multistate consortias on cataloging and metadata projects, among those are the Orbis Cascade Alliance, the University System of Georgia, and SkillsCommon.
Robin is both a practitioner with over 10 years of cataloging and a trainer. She is a frequent guest on WREK’s Lost in the Stacks discussing metadata and semantic web topics. She holds a B.A. in English from the University of Georgia; a MLIS from the University of South Carolina; certificates in Project Management (University of Georgia), and a Yellow Belt in Six Sigma (a quality and processes control standard). Her book Semantic Web Technologies and Social Searching for Librarians was published in 2012.
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