Digital Archives and AI Records Management Course

Overview

The Digital Archives and AI Records Management Course is a five-day intermediate course for records officers, administrative teams, archivists, information management teams, and compliance support functions who leave with a Digital Archive AI Improvement Plan. The course connects records requirements, lifecycle controls, metadata, classification, digitization quality, AI enrichment, semantic search, access, integrity, preservation, and human validation. Agile Leaders Training Center delivers this course on digital archives and AI records management.

Who Should Attend

• Records functions responsible for capture, classification, retention, and disposition
• Archive functions responsible for description, preservation, access, and discovery
• Information management functions responsible for metadata, repositories, and retrieval
• Administrative functions responsible for digital files and transfer to record systems
• Governance functions responsible for access, evidence, quality, and oversight

The course assumes participants manage records or digital collections and leaves out archival conservation science, software engineering, and jurisdiction-specific legal advice.

Departments and Industries

The course supports digital archive and records decisions across public, private, and nonprofit environments.

• Archives, libraries, and knowledge services
• Corporate records and information governance
• Healthcare and education administration
• Financial and professional services
• Engineering, energy, and infrastructure organizations

Learning Objectives

By the end of this course, participants will be able to:

• Analyze digital archive requirements across the records lifecycle
• Build metadata, classification, access, and retention controls
• Apply AI to description, tagging, search, and sensitive-content review
• Evaluate generated metadata for accuracy, context, integrity, and bias
• Prioritize preservation and archive improvement actions
• Create a Digital Archive AI Improvement Plan

Course Agenda

Day 1: Records and Archive Context

• ISO 15489 Records Management Principles Map
• Business Context and Records Requirement Analysis
• Record Identification and Capture Decision Matrix
• Digital Records Lifecycle and Control Model
• Archive Stakeholder and Access Needs Register

Day 2: Metadata and Classification

• Archival Description and Metadata Element Canvas
• File Plan and Classification Scheme Design
• Controlled Vocabulary and Entity Authority List
• Retention and Disposition Rule Register
• Metadata Quality and Consistency Checklist

Day 3: AI Enrichment and Discovery

• AI Tagging and Topic Extraction Workflow
• Automated Description and Summary Review Method
• Semantic Search and Retrieval Test Plan
• Sensitive Information Detection and Escalation Grid
• Human Validation and Professional Judgment Protocol

Day 4: Integrity Access and Preservation

• Digitization Quality and Completeness Assessment
• Authenticity Provenance and Audit Trail Map
• Role-Based Access and Use Permission Matrix
• Digital Preservation Risk and Format Register
• AI Use Governance and Monitoring Checklist

Day 5: Digital Archive Improvement Practice

• Suggested Exercise: Records Requirement and Lifecycle Review
• Suggested Exercise: Metadata and Classification Quality Challenge
• Suggested Exercise: AI Description and Search Validation
• Suggested Exercise: Integrity Access and Preservation Decision
• Capstone Exercise: Digital Archive AI Improvement Plan

Practical Exercises

The course includes suggested activities for improving digital archives while maintaining records controls and professional judgment.

• Suggested activity: classify a set of digital materials and document capture decisions.
• Suggested activity: design metadata and test AI-generated tags against the source content.
• Suggested activity: evaluate semantic search results and sensitive-information flags.
• Suggested activity: present a Digital Archive AI Improvement Plan with owners, controls, measures, and priorities.

FAQs

Who suits the Digital Archives and AI Records Management Course?

Records officers, archivists, administrative teams, information managers, and governance support functions suit the course; it assumes responsibility for records or digital collections.

How does AI records management differ from a general digitization course?

AI records management combines capture, lifecycle, metadata, classification, access, integrity, retention, preservation, AI enrichment, search, and validation rather than focusing only on scanning.

How can AI support digital archives?

AI can support tagging, topic and entity extraction, draft descriptions, semantic search, retrieval, classification assistance, and sensitive-content detection when archival professionals validate results.

Why must AI-generated archival metadata be reviewed?

Generated metadata requires review because archival meaning depends on source context, provenance, relationships, terminology, accuracy, access conditions, and professional judgment.

What belongs in a Digital Archive AI Improvement Plan?

A Digital Archive AI Improvement Plan links archive needs, lifecycle controls, metadata, AI use cases, validation, access, integrity, preservation risks, owners, measures, and phased actions.

Conclusion

Participants leave with a Digital Archive AI Improvement Plan connecting records requirements, metadata, AI enrichment, access, integrity, preservation, and ownership. The work product improves how digital materials are controlled and discovered. It gives archive teams a reusable structure for quality checks, human validation, risk decisions, monitoring, and phased improvement.