AI Literacy: Three-credit, Semester-long course on AI Literacy

Below is a sample course guide for a three-credit, semester-long course on AI Literacy designed and taught by a librarian. This outline includes a course description, learning objectives, suggested weekly topics, assessment ideas, and key readings or resources. Instructors can adapt the scope and depth of each subject based on institutional needs and the background of enrolled students.

AI in Librarbrianship: Information Engineering of Knowledge Architecture

The Evolving Role of AI in The Library 

A new conceptual paradigm of librarianship is emerging as libraries integrate AI systems. In this paradigm, librarians are not being replaced by AI but are evolving into 'information engineers' or 'knowledge architects." They are the ones who co-design user experiences with algorithmic systems, preserving the critical role of human judgment and ethics in library services. This shift underscores the importance of aligning AI tools with the library's mission of fostering access, intellectual freedom, and cultural memory. It also highlights the unique skills and expertise that librarians bring to the table, making them integral to the future of libraries in the AI era.

AI's Promise in Libraries

• Streamlined workflows and enhanced efficiency

• Personalized patron experiences

• Advanced data curation and management

• Potential for pioneering contributions to digital scholarship

Key Concerns and Challenges

• Job displacement due to automation

• Risk of spreading misinformation

• Legal liabilities and potential regulatory issues

• Increased psychological or time-management pressures on staff

Libraries' Unique Position

• Intersection of information, technology, and ethical inquiry

• Need for robust frameworks to maintain professional integrity

• Importance of safeguarding users' rights and privacy

Balanced Strategy for Implementation

• Ongoing professional development to keep staff informed and skilled

• Human oversight integrated into AI-driven workflows

• Transparent governance structures outlining clear accountability

Reinforcing Core Library Values

• Protecting intellectual freedom and equitable access

• Maintaining public trust through responsible AI adoption

• Ensuring that traditional professional ethics guide modern technologies

Looking Ahead

• Embracing change and mitigating risks through community collaboration

• Articulating a forward-thinking vision of librarianship that balances technology with ethical and social responsibilities

• Fostering an environment where AI tools enrich library services without compromising fundamental principles

Empowering Librarians to Navigate AI-Driven Research Support: A Step-by-Step Guide

Key Takeaways

• Incremental Learning: Focus on step-by-step skill-building, starting with fundamentals before moving to more complex AI concepts.

• Collaborative Mindset: Work closely with peers, faculty, IT staff, and professional organizations to ensure well-rounded, up-to-date expertise.

• Ongoing Adaptation: AI and data science evolve quickly, so continuous training and project-based practice are crucial.

• Ethics and User-Focus: Maintain a commitment to ethical standards and user-friendly services.

By following this guide, librarians can methodically address the skill gaps between traditional library services and the rapidly expanding field of AI-driven research support. This journey enriches library staff skills and ensures that libraries remain vital and responsive hubs for scholarly innovation. 

Expanding the Role of Academic Librarians in Supporting AI Initiatives: A Comprehensive Guide

 Expanding the Role of Academic Librarians in Supporting AI Initiatives: A Comprehensive Guide

Below is a structured guide to help academic librarians expand their roles in supporting artificial intelligence (AI) initiatives and services. This framework focuses on text and data mining (TDM), advanced query mediation, and AI-enhanced discovery systems, among other key responsibilities.

The Academic Librarian's Guide to Supporting Researchers in Text and Data Mining (TDM)

 

Key Takeaways:

1. Collaboration is essential: Engage with researchers early to align on goals and constraints.
2. Licensing expertise is critical: Understand and negotiate TDM permissions within existing or new agreements.
3. Choose tools wisely. Balance open-source and proprietary options based on project scale, technical proficiency, and available resources.
4. Uphold ethical and legal standards: Ensure responsible handling of sensitive data and compliance with privacy regulations.
5. Train and support: Offer workshops, resources, and consultations to empower researchers in TDM skills.
6. Evolve continuously: Stay informed about TDM developments and adapt library services accordingly.

Implementing the strategies outlined in this guide can help librarians become indispensable partners in TDM initiatives, advancing individual research projects and the broader mission of the academic institution.