AI in Academic Libraries: Enhancing Student Success

Harnessing the Potential of AI Technologies to Enhance Student Success

Presented by Muhammad Hassan, Linda Saleh, and Craig Anderson

Introduction

The presenters discuss the integration of artificial intelligence (AI) technologies in academic libraries and learning commons to enhance student success. They emphasize the importance of embracing AI tools to support students in various aspects of their academic journey, from research assistance to skill development.

Understanding Artificial Intelligence

Muhammad Hassan introduces AI as a simulation of human intelligence processed by machines. He notes that while AI has become a popular topic recently, it has been around for a long time. Key applications of AI mentioned include:

• Expert systems
• Natural language processing (NLP)
• Machine vision
• Speech recognition

AI and Student Success

The presenters highlight the role of libraries and learning commons in supporting student success. Common student inquiries include:

• How to conduct research
• Finding articles and resources
• Achieving academic goals
• Accessing workshops and support services
• Improving well-being and efficiency

Muhammad emphasizes that addressing these needs is crucial for student success, and AI technologies can play a significant role in providing solutions.

Integrating AI into Workflows

The team discusses their proactive approach to incorporating AI into their institutional workflows:

• Providing workshops for faculty and students on proper AI usage
• Developing an AI policy to guide ethical and effective use
• Encouraging faculty to learn and embed AI tools in teaching
• Collecting and analyzing data using AI tools for insights on student behavior

Data Analysis and Predictive Modeling

Muhammad shares examples of how they use AI to analyze data:

• Tracking library usage, tutoring sessions, and resource access
• Using AI tools like ChatGPT to analyze large datasets quickly
• Applying predictive analysis to determine optimal library hours and resource allocation
• Creating heat maps to visualize peak usage times on their website

Challenges with Sentiment Analysis

He notes that while AI excels in processing data, it still struggles with sentiment analysis. Libraries need to ensure AI models are built with proper sentiment understanding and work towards correcting deficiencies.

Student Interactions with AI

Examples from the Learning Commons

Craig Anderson shares anecdotes illustrating how students interact with AI:

• A student used QuillBot, an AI tool, to find articles but received fabricated references. She was unaware that the articles were not real.
• ESL students used translation tools for assignments, which were flagged by AI detection software as plagiarized, leading to misunderstandings.
• A professor mistakenly accused students of cheating by using ChatGPT to confirm authorship of their papers, not realizing the tool can provide misleading affirmations.

Concerns and Misunderstandings

Students worry about being falsely accused of plagiarism due to AI tools. These examples highlight the need for proper education on AI usage and limitations.

When Not to Use AI

Muhammad addresses a question about situations where AI should not be used to ensure student success:

1. Foundational Learning: In programming courses, students should first learn to code without AI assistance to build a solid understanding.
2. Writing Skills: In writing-intensive courses, reliance on AI can hinder the development of essential writing abilities.
3. Communication Skills: In communication classes, students benefit more from interacting with peers rather than AI.

He emphasizes that AI should enhance, not replace, foundational learning and interpersonal interactions.

AI as a Supplementary Tool

Analogy with Calculators

Craig draws an analogy between AI tools and calculators in education:

• Just as calculators are introduced after students understand basic arithmetic, AI should be used after foundational skills are developed.
• AI can then serve as a tool to enhance and advance learning.

Embracing AI Literacy

Linda Saleh discusses the importance of AI literacy and how AI tools can supplement student learning in areas beyond research:

• Reading and comprehending scholarly articles
• Preparing presentations and participating in scholarly conversations
• Developing coding skills

AI Tools for Skill Development

Reading Assistance

Linda highlights AI tools that help students understand complex academic texts:

• ChatPDF: Allows students to upload PDFs and ask questions to gain better understanding.
• SciSpace: Provides access to open-access scholarly articles with a co-pilot feature for interactive learning.

Presentation and Public Speaking

AI tools can assist students in creating and delivering effective presentations:

• SlidesGo, Clipchamp, SlidesAI: Help in developing visual presentations.
• Udly: An AI tool that provides feedback on practice speeches, suggests improvements, and anticipates audience questions.

Coding Assistance

AI tools like Blackbox AI support students in learning programming by offering coding assistance and troubleshooting help.

Balancing AI Use and Critical Thinking

In response to concerns about AI potentially hindering critical thinking skills, the presenters emphasize:

• AI tools should be part of a broader set of resources available to students.
• Faculty and support services play a crucial role in ensuring students continue to develop essential skills independently.
• Teaching students how to use AI properly is vital for their success in an evolving technological landscape.

Ethical Considerations and Policy Development

The presenters acknowledge the importance of discussing the ethics of AI use in education:

• Institutions should have conversations about AI ethics at the start of each semester.
• Developing clear policies and guidelines helps prevent misuse and misunderstandings.
• Emphasizing transparency, authorship, and copyright considerations is essential.

Conclusion

The team concludes by reinforcing the potential of AI technologies to enhance student success when used appropriately. They advocate for defining what success means for students and then integrating AI tools thoughtfully to support that vision.

The Boundaries of Authorship: Can AI Be Considered an Author?

Generative AI and Authorship

Presented by Robin Kear, Academic Librarian at the University of Pittsburgh

Introduction

Robin Kear discusses the question: Can generative AI (GenAI) be an author? She explores the implications of this question, considering the rapid advancement of AI technology and its impact on authorship, creativity, and responsibility.

Can GenAI Be an Author?

Kear reflects on her concerns regarding AI's potential to become sentient or possess its own consciousness and agency. She believes that, with the current structure of generative tools, the answer is no. GenAI reacts, suggests, anticipates, and amalgamates existing content but does not create something entirely new.

AI-Generated Content and Authorship

Using an example of an image created by a human using DALL-E (an AI image generator), Kear prompts the audience to consider where authorship resides in such creations. She emphasizes the importance of understanding the human aspects of being an author and creator.

What Makes an Author?

Kear identifies four key human aspects of authorship:

1. Creativity: The idea must originate from the individual. While influenced by experiences and environments, humans create new things that didn't exist before.
2. Agency: Authors have the will to decide what to do with their ideas, choosing how, when, and what to produce.
3. Moral Responsibility: Authors are morally accountable for what they put into the world, and their work should be discoverable and attributable to them.
4. Legal Responsibility: Authors accept legal responsibility for their creations in the public and economic spheres, including the publishing industry.

Research on AI and Authorship in Academic Journals

Kear shares a research project conducted with colleague Amy Jenkins, examining how research journals are addressing AI and authorship. They analyzed top journals across various disciplines to find policies and guidance on AI authorship.

Methodology

• Used Journal Citation Reports to identify impactful journals.
• Selected top three journals in chosen categories based on impact factor.
• Searched journal and publisher websites for AI authorship policies.

Findings Based on the Four Aspects of Authorship

Creativity and Agency

• AI Cannot Be an Author: All journals agreed that an author must be a human being.
• Lack of Agency: AI does not have the ability to act independently or be accountable.
• AI in Images: Generally not permissible, especially in scientific contexts due to potential harm to scientific advancement.
• Writing Assistant vs. Data Analysis: A nuanced difference exists between using AI as a writing tool and using it for data insights, which requires disclosure.

Moral Responsibility

• Personal Accountability: Authors must be accountable for their content, hence AI cannot be an author.
• Disclosure Requirement: Use of AI tools must be disclosed, with specifics on how and where it was used.
• Publication Process: Different guidelines exist for authors, peer reviewers, and manuscript reviewers.
• Confidentiality Concerns: Public AI tools like ChatGPT should not be used for peer review due to confidentiality and proprietary rights.

Legal Responsibility

• Liability: Journals could be held liable for AI-generated content, so responsibility is shifted to the author.
• Verification: Authors are responsible for verifying the accuracy of AI-generated content, including potential errors or plagiarism.
• Ethical Breaches: Authors are liable for any breaches of publication ethics, even if AI tools were used.
• Guidance from COPE: The Committee on Publication Ethics emphasizes authors' full responsibility for their manuscripts.

Reconsidering the Role of AI in Creative Endeavors

Kear poses critical questions about how we should view AI in the context of creativity:

• Should AI be considered an assistant or helper rather than a creator?
• Can AI serve as a sounding board for ideas or help augment human creativity?
• Where is the ethical line between presenting something as one's own idea versus a technology-created idea?
• Given that AI responses are derivative, what is its usefulness in creative work?

Reflection on Automated Creativity

She references the 1982 World's Fair painting robot as an early example of automated creativity, noting that while simplistic compared to current AI, it prompts consideration of the evolving role of technology in authorship.

Further Considerations

Kear discusses additional points stemming from her findings and university discussions:

• Changing Acceptance: The use of AI in writing may become more accepted over time, potentially becoming seamless and expected.
• Reflecting Existing Challenges: AI often mirrors societal biases and existing challenges related to transparency, integrity, and accountability.
• Core Principles: The fundamental principles of research and publishing should continue to guide the use of AI in authorship.

Question and Answer Session

To What Extent Do Humans Also Derive from Other Content?

Response: Kear acknowledges that humans are influenced by their environment and existing works. In academic writing, literature reviews are essential for building upon previous research, but authors strive to contribute something new to the conversation.

At What Point Is AI Used or Not Used?

Response: She differentiates between general writing tools (like Microsoft Editor or Grammarly) and generative AI tools. While tools like Microsoft Co-Pilot are still developing, she focuses on the implications of generative AI in authorship.

If a Student Uses an AI Tool to Fully Write a Paper, Who Is the Author?

Response: Kear advises against students using AI to write entire papers. Such papers may contain inaccuracies, lack depth, and could be easily identified by instructors. Students should be cautious about relying on AI for academic work.

Future Value of Writing in Editing vs. Writing Itself

Response: Currently, the value of generative AI lies in its ability to assist rather than replace human creativity. She mentions authors using AI tools based on their own work to aid in writing, but emphasizes that AI should complement, not replace, human authorship.

Conclusion

Kear concludes by emphasizing the importance of maintaining core principles in research and publishing as AI continues to evolve. Transparency, integrity, attribution, and accountability should guide any use of AI in authorship and creative endeavors.

AI in Education: How Librarians Can Lead the Way

Navigating AI in Education through a K-12 Librarian's Lens

Presented by Delandra Seals, Teaching and Learning Librarian at the University of North Carolina at Wilmington

Introduction

Delandra Seals shares insights on integrating artificial intelligence (AI) in K-12 education from a librarian's perspective. With a background in K-12 education, special education, public libraries, and higher education, she brings a comprehensive view of how AI can enhance teaching and learning.

Understanding the Evolution of AI

AI is Not New

• AI has been gradually integrated into everyday life over the years.
• Examples include predictive text, speech-to-text, smart devices like Alexa and Siri, and self-driving cars.
• Students are already interacting with AI through various technologies.

Defining AI

• AI refers to computers programmed to perform tasks that typically require human intelligence.
• Involves algorithms, machine learning, data patterns, and predictive modeling.
• Used in applications like facial recognition, red-light cameras, and digital assistants.

AI in Education

The Potential of AI

Sal Khan, founder of Khan Academy, envisions AI as a transformative tool in education, providing personalized tutoring to every student.

Historical Disruptions in Teaching

• Technologies like calculators, search engines, and Google Translate have previously disrupted education.
• Matt Miller emphasizes that education adapts and moves forward with new technologies.

Teachers' and Students' Perspectives

• Teachers are curious about integrating AI into the classroom and concerned about academic integrity.
• Students are interested in using AI to assist with assignments and learning challenges.
• IT staff are evaluating the implications of AI on network security and educational policies.

Introducing ChatGPT and AI Tools

What is ChatGPT?

• ChatGPT is a language model developed by OpenAI.
• G: Generative – capable of generating text.
• P: Pre-trained – trained on large datasets to understand language patterns.
• T: Transformer – uses transformer architecture to process input and generate responses.

Capabilities and Limitations

• Generates human-like text based on input prompts.
• Can assist with lesson planning, idea generation, vocabulary lists, writing prompts, and feedback.
• Limitations include potential biases, inaccuracies, outdated information (knowledge cutoff), and lack of ethical judgment.
• Not designed for users under certain age thresholds due to privacy policies.

Privacy and Ethical Considerations

• Privacy policies are crucial, especially in K-12 education (FERPA considerations).
• Most AI tools are designed for users aged 13 or older.
• Educators should review privacy policies before integrating AI tools into the classroom.

Practical Applications of AI in Education

Using AI Tools

• Teachers and librarians can use AI for creating lesson plans, assessments, and instructional materials.
• Examples include generating open-ended questions, scaffolding for English Language Learners (ELLs), and drafting communications.
• AI can assist with administrative tasks like writing report card comments and responding to emails.

Prompt Engineering

• The quality of AI-generated output depends on the specificity of the input prompts.
• More detailed prompts yield more accurate and useful results.
• Example: Asking Google Gemini to generate open-ended questions about "Long Way Down" by Jason Reynolds.

Examples of AI Tools

• Google Gemini: An AI tool for generating text and ideas.
• Bing Chat: Uses GPT-4 for search and conversational responses.
• Microsoft Co-Pilot: Integrates with Microsoft Office for productivity enhancements.
• YouChat: An AI-powered search assistant that can generate code, answer questions, and assist with tasks.
• TinyWow: A tool for converting documents and media files.
• Curipod and MagicSchool AI: Generate interactive lesson plans and presentations based on standards and grade levels.
• Canva: Offers AI features for creating graphics and documents.

Addressing Plagiarism and Academic Integrity

• Tools like Turnitin and GPTZero can detect AI-generated text.
• Educators should establish policies on AI usage and plagiarism with their school communities.
• Encourage transparency and ethical use of AI among students.

Best Practices for Integrating AI

Crafting Effective Prompts

• Be clear about the context, purpose, audience, and desired outcome when writing prompts.
• Use frameworks like CRAFT (Context, Role, Audience, Format, Topic) to structure prompts.
• Example: "As an expert fourth-grade math teacher, create a lesson plan on fractions aligned with [specific standard]."

Human Oversight and Critical Thinking

• AI is a tool to assist educators, not replace them.
• Educators must review and verify AI-generated content for accuracy and bias.
• Emphasize the development of creativity, critical thinking, problem-solving, empathy, and human interaction, which AI cannot replicate.

Policy Development

• Work with school districts to develop policies regarding AI usage.
• Consider the ethical implications and establish guidelines for students and staff.
• Promote an environment where students feel comfortable discussing their use of AI tools.

Conclusion

AI offers numerous opportunities to enhance education by improving productivity, organization, and addressing learning gaps. Educators should embrace AI as a partner in the educational journey, leveraging its capabilities while maintaining human oversight and fostering essential skills in students.

The Impact of AI on Academic Library Research Support: Perceptions and Realities

The Impact of AI on Academic Library Research Support Services and Literature Review

Introduction

This article explores the impact of artificial intelligence (AI) on academic library research support services, with a particular focus on literature reviews. The discussion includes perceptions of academic librarians towards AI, the promotion and evaluation of AI tools, and the integration of AI into the literature review and research process.

Perceptions of Academic Librarians Towards AI

Initial Surveys and Findings

Surveys conducted between 2020 and 2022 indicated that librarians generally viewed AI as a helpful tool that would not jeopardize their employment status. Key findings included:

• 30% of librarians did not expect significant impact from AI on library functions.
• Little impact was anticipated on instruction (30%) and references.
• Greater concern was noted regarding collection development.
• 67% believed AI would transform library functions positively.

Changing Perspectives in 2023-2024

Recent surveys from 2023-2024 show a shift in perceptions:

• Only 14% of librarians believed students used AI for research.
• Contrastingly, 73% of students confirmed using AI in their courses, with 68% admitting to inappropriate use.
• Approximately 38% of librarians felt AI made them lazy and threatened their employment.

This highlights a collision between librarians' perceptions and students' actual use of AI, indicating a need for librarians to adapt to the changing landscape.

Trust and Understanding of AI

Librarians exhibit varied attitudes towards AI:

• Some view AI as a "magic box" that works without needing to understand its inner workings.
• Others prefer to collaborate, test, and evaluate AI tools before adopting them.
• Trust issues arise from a lack of understanding or skepticism towards new technologies.

Technology Hype Cycle and AI

The Gartner Hype Cycle places generative AI in the "Peak of Inflated Expectations" stage, suggesting that while expectations are high, practical results may not yet meet the hype. This underscores the need for real research into AI's effectiveness in library settings.

Impact on Teaching, Learning, and Research

Librarians are concerned about AI's implications for:

• Teaching and learning processes.
• Discovery and research synthesis.
• Issues of copyright, privacy, and bias.
• Agency and authorship in academic work.
• The future of reference and instruction services.

Positionality and Personal Engagement with AI

The Librarian's Multiple Roles

The presenter identifies as a librarian, teacher, technologist, and researcher, wearing many hats in the academic environment.

Adoption of AI Tools

Using Rogers' Diffusion of Innovation Theory, the presenter places themselves as an early adopter, having moved through awareness and interest stages to evaluating and adopting AI tools in literature review processes.

Despite being an early adopter, the presenter notes that they are surrounded by prudent individuals who are cautious or distrustful of AI.

Promotion and Evaluation of AI Tools

Library Promotion of AI Products

A survey question from Helper Systems asked, "Does your library currently offer or promote any AI products to researchers?" Findings included:

• A slight increase in libraries promoting AI products from 13% in 2023 to 19% in 2024.
• A significant decrease in libraries not promoting AI, indicating growing interest.

Personal Initiatives in Promoting AI

The presenter actively promotes AI tools and integrates them into practice and teaching:

• Created a directory and evaluation of semantic search engines in 2022.
• Presented on the automation of systematic reviews using AI tools.
• Developed a popular guide on using AI for literature review, published in November, which has garnered over 1,500 users.
• Participated in beta testing and consulting for AI tool development, providing valuable feedback to developers.

AI in Literature Review and Research Process

Benefits of AI in Literature Reviews

AI tools offer significant advantages in conducting literature reviews, especially systematic reviews that involve analyzing thousands of scholarly records:

• Shortens the time required for literature searches and analysis.
• Assists in text mining and data synthesis.
• Enables smaller teams to handle large-scale reviews efficiently.

Teaching and Supporting Students

The presenter has redesigned literature review courses and micro-credentials to incorporate AI tools, helping students who often spend excessive time on literature reviews due to:

• Difficulty in searching effectively.
• Challenges in analyzing and synthesizing information.

Ongoing Research and Development

Current projects include:

• Publishing a taxonomy and characteristics of AI discovery tools, highlighting their features, limitations, and suitability in the research process.
• Developing an AI research assistant based on the Hopscotch Research Design Model, providing a step-by-step framework for research.
• Working on an AI recommendation system for educational researchers.

Interactive Research Methods Lab

The presenter is a member of an Interactive Research Methods Lab, which received an innovation award for incorporating library and open access resources with an AI recommendation system. Current work involves:

• Developing a research assistant using ChatGPT and customized language models.
• Creating custom chatbots tailored to specific research needs.

Guides and Resources

The presenter has developed guides to assist in discovering new literature using AI tools:

• Categorizing tools based on research processes and literature review steps.
• Including AI tools for research planning, such as developing research questions and conceptual frameworks.
• Reviewing AI search engines and research assistants, comparing features and limitations.
• Highlighting hybrid systems that integrate various AI technologies.

Collaborations and Institutional Support

Emphasizing the importance of collaboration, the presenter notes involvement in various institutional initiatives:

• Member of the Office of Research Applied Technology Community, discussing AI topics.
• Part of the Scholarship of Teaching and Learning (SoTL) group, working on AI tools to support teaching and learning scholarship.
• Engagement with research labs in computer science and data analytics, with plans to offer a new master's program in AI.
• Collaboration with the Digital Learning department to create resources for instructors on teaching with or without AI.

Future Aspirations

The presenter expresses a desire for an AI lab to experiment with different tools and assess their applicability in education and library services.

Conclusion

AI is increasingly impacting academic library research support services, particularly in literature reviews. While librarians' perceptions of AI are evolving, the presenter advocates for proactive engagement with AI tools to enhance research processes and support students and faculty effectively.

References

A list of reference materials is available for further reading.

Questions and Discussion

During the presentation, the following questions were addressed:

Question from Jenny Pierce:

Are you using literature review and systematic review interchangeably?

Answer: No, they are separate. The presenter has created distinct guides for traditional narrative literature reviews (commonly required for dissertations, theses, or capstones) and systematic reviews, which often require expensive platforms that some students cannot afford.

Question from Rachel:

We have been beta testing an app called CurvXR to develop support for students learning using virtual reality. Some of these science anatomy and chemistry models are impressive. What are your thoughts?

Answer: Agrees that integrating tools like virtual reality (VR) and augmented reality (AR) with AI is beneficial. Involvement in applied technology communities discusses combining different tools, and integration between technologies and people (teachers and students) is the next step. Customization of AI tools like ChatGPT is growing, offering more tailored solutions for organizations and specific purposes.

Final Remarks

The presenter invites further questions and encourages collaboration in exploring the impact of AI on academic libraries.

The Future is Now: Exploring AI in Public Libraries

Exploring AI in Public Libraries: Programs for Communities

Presented by Arya Mala Prasad and colleagues from the Center for Technology in Government at the University at Albany

Introduction

This presentation delves into the research conducted by the Center for Technology in Government (CTG) at the University at Albany, focusing on the role of public libraries in fostering critical and inclusive civic engagement in artificial intelligence (AI) initiatives. The research team includes:

• Arya Mala Prasad, Researcher at CTG
• Zongshang Zhang, PhD student at Rockefeller College of Public Affairs and Policy, and Graduate Assistant at CTG
• Mila Gasco Hernandez, Research Director at CTG and Associate Professor at Rockefeller College
• J. Ramon Gil-Garcia, Director of CTG and Professor at Rockefeller College

Background and Motivation

AI Bias and Public Engagement

• Increasing use of AI in various sectors such as financial services, healthcare, welfare programs, and policing.
• Evidence of racial and other biases in AI decision-making processes.
• Efforts at national and international levels to strengthen regulation and governance of AI systems.
• Public engagement is seen as a mechanism to improve transparency and accountability in AI systems.

Challenges in Facilitating Public Engagement

• Lack of technical knowledge among the general public to understand AI.
• Need for open and accessible spaces for public participation in AI initiatives.

The Role of Public Libraries

• Public libraries have a history of promoting digital literacy and ensuring digital inclusion and equity.
• They offer safe and collaborative spaces for communities to discuss local issues, including the impacts of AI.
• Libraries can empower marginalized communities to understand and engage with AI technologies that affect them.

Research Objectives

The research aims to answer the following questions:

1. What role may public libraries play in increasing knowledge about AI in the community?
2. How may public libraries foster inclusive civic engagement in AI initiatives?
3. What are the opportunities, threats, benefits, and challenges of public libraries leading inclusive civic engagement in AI initiatives?

This research is part of a larger project funded by the Institute of Museum and Library Services (IMLS) and conducted in partnership with the Urban Libraries Council (ULC). The project began in August 2023 and will continue until Spring 2026.

Focus of the Current Study

The presentation focuses on the initial mapping exercise aimed at identifying and assessing the role of public libraries in increasing AI awareness and fostering inclusive civic engagement.

Specific Research Questions

1. What are the main types of AI programs and services offered in public libraries?
2. What is the purpose of AI programs and services, and who are the intended users?
3. What are the main components of AI programs and services?
4. Do the AI programs and services include individuals from marginalized communities and address the potential negative effects of AI systems?

Scope Clarification

The research focuses solely on AI programs organized for community members, excluding AI services or programs used internally by libraries for operations (e.g., search catalogs, robots, voice assistants).

Methodology

Data Collection

Data collection included three steps:

1. Review of Library Associations: Searched publications and resources from the American Library Association (ALA), Urban Libraries Council (ULC), and the International Federation of Library Associations (IFLA) to identify popular AI programs and success stories.
2. Systematic Website Review: Examined the websites of ULC member libraries to find AI-related events, programs, and blogs.
3. Broad Internet Search: Conducted Google searches using keywords identified from library websites (e.g., "ChatGPT courses") to uncover additional programs.

Data collection spanned from November 2023 to February 2024, including programs that were announced or available online during or before this period. The dataset comprised 109 cases, with 97 from the United States and 12 from Canada.

Data Analysis

An inductive approach was used to classify the cases into different categories based on:

• Purpose of the AI programs
• Targeted participants
• Types of partnerships involved
• Content and components of the programs

Findings

Main Purposes of AI Programs in Public Libraries

1. Increasing Awareness of AI: Informational programs aimed at providing a basic understanding of AI, including lectures, courses, and seminars that explain AI terminologies, technologies, benefits, and challenges.
2. Providing Technical Training on AI: Instructional programs focused on teaching community members how to use AI applications or tools (e.g., ChatGPT, DALL·E) and offering coding classes related to AI programming.

Types of AI Programs Offered

1. Increasing Awareness Programs

• Lectures and Courses: The most common type, featuring one-way communication from experts to the audience. Examples include:
  • AI for Communities Program: Offered by Brooklyn Public Library and San Mateo Public Library in collaboration with Women in AI Ethics, covering AI basics, generative AI, and online safety.
  • ABC of AI: An introductory course by San Jose Public Library, explaining AI terminologies and discussing benefits and risks.
• Seminars and Conversations: Interactive discussions between participants and experts. Examples include:
  • Building the World We Want: A panel discussion on global AI governance hosted by the New York Public Library.
  • Conversation with Experts on AI: Organized by William F. Laman Public Library, featuring local university researchers.
• Exhibitions: Interactive displays or art installations to engage the community with AI concepts. Examples include:
  • The Laughing Room: An interactive art exhibition at Cambridge Public Library in collaboration with Harvard University, demonstrating AI's ability to detect humor through voice inflections.
  • Misinfo Day Escape Room: Hosted by St. Joseph County Public Library in partnership with the University of Washington, teaching participants to identify bots, deepfakes, and misinformation.
• Podcasts: Audio programs discussing AI topics. Examples include:
  • AI Podcast Series: By Knox County Public Library, a four-part series breaking down AI in everyday life (e.g., self-driving cars, robots).
  • Tech Talk Weekly: A 20-minute weekly podcast by Broward County Public Library, covering AI as part of broader tech news.

2. Technical Training Programs

• Hands-On Workshops: Practical sessions teaching participants to use AI tools or programming skills.
  • Application of AI Tools: Workshops on using generative AI tools for professional skills or hobbies.
    • Example: "Using ChatGPT for Writing Effective Resumes and Cover Letters" by Brooklyn Public Library.
    • Example: Digital art creation using DALL·E at St. Louis Public Library.
  • Programming and Coding Workshops: Teaching AI programming skills.
    • Example: "After-School AI Program" at St. Joseph County Public Library, teaching coding and machine learning to teenagers.
    • Example: Hands-on AI and machine learning workshop at San Jose Public Library, culminating in participants developing their own machine learning projects.
• Maker Space Programs: Providing access to AI-related devices and kits for experiential learning.
  • AI Maker Kits: Offered by Frisco Public Library, allowing patrons to experiment with AI technologies (recipient of a national award).
  • Tech Petting Zoo: Hosted by an unspecified library, offering devices like AI gadgets, virtual reality equipment, and 3D printers for hands-on exploration.

Role of Collaboration and Partnerships

Partnerships play a crucial role in organizing AI programs, with over 50% of libraries collaborating with external entities. Types of partners include:

• Universities: Collaborations involve inviting experts for talks or co-hosting courses and exhibitions.
  • Example: New York University partnering with Queens Public Library to offer a five-week series on AI, focusing on ethical aspects and empowering public advocacy.
• Nonprofits: Libraries leverage resources or co-host events with nonprofits.
  • Example: Women in AI Ethics collaborating with multiple libraries for the "AI for Communities" course.
  • Example: Code.org's "AI for Oceans" game used by libraries to teach kids about machine learning and data's role in AI.
• Businesses: Industry experts are invited for lectures and workshops.
  • Example: Seattle Public Library's "Tech Talk 101" series featuring startup founders discussing emerging technologies, including AI.
• Government Agencies: Limited but notable involvement.
  • Example: Boston Public Library partnering with the Mayor's Office to organize an AI course.
  • Example: Some government agencies sponsoring AI courses at local public libraries.

Observations and Opportunities

Current State

• Public libraries are beginning to offer AI programs to increase awareness and provide technical training.
• Most programs are one-off events or short courses rather than structured, long-term initiatives.
• Programs often include discussions on the benefits and challenges of AI, focusing on relatable technologies like ChatGPT and voice assistants.
• Libraries address the needs of different age groups, offering sessions for teens, adults, and seniors.

Potential for Expansion

• Opportunity to develop more structured and long-term AI programs similar to existing digital literacy classes.
• Need to tailor programs for marginalized communities to help them understand how AI systems impact them, especially concerning biased decision-making.
• Lack of civic engagement opportunities within current programs; potential to include co-creation activities and facilitate broader community discussions on AI.
• Example from Spain: The "ExperimentAI" program offered a 15-session course with co-creation opportunities, allowing participants to work with professionals to address real-world problems using AI.

Conclusion

The research indicates that while public libraries are starting to play a role in increasing AI awareness and providing technical training, there is significant room for growth. By expanding programs to include marginalized communities and fostering civic engagement, libraries can become pivotal in shaping an inclusive AI future.

Next Steps

• Continue researching the role of public libraries in AI education and civic engagement.
• Explore opportunities to collaborate with libraries in developing and implementing more inclusive and participatory AI programs.
• Assess the impact of these programs on communities, especially marginalized groups.

Stay Connected

If you're interested in this research, you can follow the Center for Technology in Government (CTG) for updates:

• X (formerly Twitter): @ctgalbany
• LinkedIn: CTG at University at Albany
• Website: ctg.albany.edu

Acknowledgments

Special thanks to San Jose State University and Future of Libraries for organizing the conference on AI and Libraries, and to the Institute of Museum and Library Services for funding the research.