Alternatives to Generative AI for Library Research

Introduction

The presentation explores alternatives to generative AI tools for library research, examining various AI-based research assistant tools that help locate, analyze, and synthesize academic articles. Unlike traditional generative AI like ChatGPT, these tools emphasize assisting researchers in discovering and synthesizing scholarly literature rather than creating textual outputs. The speakers aim to demonstrate three primary research assistant tools—Consensus, Research Rabbit, and Scite—highlighting their practical applications in library research and how they can empower researchers in their academic pursuits.

Definitions and Key Concepts

The presentation opens by distinguishing between two types of AI in the academic context:

• Research Assistant AI: Tools designed to assist researchers by locating, analyzing, and visualizing academic articles. They are more focused on synthesis than generation.
• Generative AI: Tools focused on creating textual content or answers, which is increasingly becoming a part of research assistant AI systems.

Rationale for AI in Libraries

Duncan and Fay explain their motivation for exploring these tools. With the rise of ChatGPT and its widespread use, particularly for generating fake or unreliable sources, librarians received many inquiries from faculty concerned about the reliability of AI-generated content. Thus, The presenters explored how AI can help researchers (faculty and students) conduct efficient library research without falling into common pitfalls.

Part 1: Defining Research Assistant AI vs. Generative AI

The presentation begins by distinguishing research assistant AI from generative AI:

• Research Assistant AI refers to AI tools designed to assist researchers in locating, synthesizing, and analyzing scholarly materials. These tools typically aim to streamline the research process, focusing on information retrieval, pattern recognition, and generating summaries of existing literature.
• Generative AI, like ChatGPT, focuses on producing new content, including written text, based on prompts provided by the user. While this technology evolves, concerns still need to be addressed over its propensity to fabricate information, such as generating nonexistent sources, misleading citations, or even biased content.

Duncan and Fay highlight the blurring of these two categories as more research assistant tools begin incorporating generative capabilities. This hybridization is becoming a key trend as developers integrate text-generation features into AI systems initially designed solely for data retrieval or analysis. This trend could significantly change how researchers conduct their work, offering new data synthesis and interpretation possibilities.

Part 2: Motivation for Presenting AI Tools in Research

The speakers explain their motivation for the presentation, stemming from the explosion of interest in ChatGPT and other generative AI platforms in 2023. This surge in interest was fueled by the increasing capabilities of these AI systems, particularly in generating human-like text. However, this also led to concerns about the reliability of AI-generated content, especially in academic research. Faculty and students were curious about AI but often needed clarification on misinformation or overly ambitious claims regarding AI's capabilities. 

A significant issue was fabricated sources generated by AI models, which raised alarms among librarians striving to maintain rigorous research standards. To address these concerns, Duncan and Fay explored how AI can legitimately support researchers without sacrificing scholarly integrity. They aim to highlight tools that can assist in research workflows without generating misleading information.

Part 3: The Rapid Evolution of Research Assistant AIs

One key observation the presenters make is how rapidly the field of research assistant AI is evolving. A year prior, tools like Elicit were free and widely accessible; however, they have since become commercial products, limiting access for students and researchers. 

Additionally, they noted a need for AI tools for humanities research in early 2023, a significant limitation for researchers in these fields. However, this has since changed with the emergence of platforms like JSTOR AI, explicitly catering to humanities research, demonstrating the rapid evolution and diversification of AI tools in the academic research space.

This fast-paced evolution has made it difficult for libraries to keep pace with developments. However, Duncan and Fay emphasize the importance of being adaptable and continually reassessing AI tools for their usefulness in an academic context.

Part 4: Key AI Tools for Library Research

The speakers then introduce three specific AI tools designed to support research:

The Two Research Assistant AI Tools

1. Consensus AI
   • Focus: Designed to answer research questions quickly by identifying scholarly claims.
   • Corpus: Primarily pulls data from Semantic Scholar, an extensive academic database focused on sciences and social sciences.
   • Key Feature: It uses a "consensus meter" to show whether articles collectively support or refute a given research question. This helps visualize the degree of consensus among scholarly articles.
   • Strength: Helps locate articles, tags high-quality papers, and provides simple visual summaries of the research.
   • Limitations: Currently focused on sciences and social sciences, needing more coverage of humanities or arts.
2. Research Rabbit
   • Focus: A literature-mapping tool that visualizes connections between academic papers and their networks.
   • Key Features:
     • It offers interactive visualizations and networked nodes that show relationships between papers.
     • Builds knowledge maps by referencing papers from Semantic Scholar and PubMed.
     • Users can explore an author's research output and examine connections between similar papers.
     • Enables collaboration by allowing researchers to share literature maps with peers.
   • Strength: Visual, interactive format allows users to see relationships between articles, making it easier to understand the broader research landscape.
   • Limitations: Its learning curve can make it challenging to use and may not be intuitive for all users.

Part 5: Demonstration of Tools

Duncan and Fay provide a live demonstration of the tools, showcasing their practical applications:

1. Consensus: Demonstrating how it returns a limited selection of papers, providing a consensus summary on whether "learning styles affect academic success in college." They caution that while the visual consensus meter is helpful, users should always read the actual papers for context.
2. Research Rabbit: They show how to create a collection of research papers on "learning styles and college students," using the tool's mapping features to find similar work and identify research networks across time. The visualization feature highlights relationships between papers, and the interactive timeline shows how the research has evolved.
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Part 6: Pros and Cons of Research Assistant AI Tools

Pros and Cons of Research Assistant AI Tools

Pros:

1. Interactive Visualizations: Tools like Research Rabbit offer a highly interactive interface that helps researchers visualize connections between papers.
2. Custom Summaries: AI tools can generate summaries, helping researchers quickly grasp the content and relevance of articles.
3. Identification of High-Quality Sources: Features like consensus meters or innovative citations allow researchers to quickly identify highly-cited or reputable papers.

Cons:

1. Learning Curve: The tools work differently than traditional library databases, confusing users who are more familiar with established methods.
2. Account Requirement: Most of the AI tools

Part 7: Future of AI in Libraries: Scopus AI and JSTOR AI

The presenters speculate on the future of AI in libraries, pointing to the release of Scopus AI and JSTOR AI as indicators that large academic publishers are integrating AI into their databases. While initially skeptical, Duncan and Fay now recognize that significant players in scholarly publishing are likely to continue developing and acquiring AI tools.

They note that JSTOR's AI platform, currently in beta, is stimulating because it focuses on the humanities, a traditionally underserved area in AI-driven research tools.

Libraries and AI Response: Strategic Approaches for Library Services

The YouTube video titled "Libraries and AI Response: Strategic Approaches for Library Services" delves into the role of artificial intelligence (AI) within libraries and offers strategic insights on how libraries can develop AI strategies. Presented by Don Means, Andrew Cox, and Eric Bokenstein, the video highlights the challenges, opportunities, and potential future role libraries could play in the era of AI. It equips you with the knowledge to navigate this evolving landscape.

Introduction

The discussion begins with a general overview by Don Means, emphasizing the Swiss-army-knife role of libraries as multifaceted institutions. Libraries do far more than manage books—they offer various public services that often go unnoticed. The video focuses on how libraries can respond strategically to AI in their services, with examples of current AI implementations and future trends.

The Role of Librarians in AI

One of the recurring themes is the evolving role of librarians in the age of AI:

• Curators of Information: Librarians could become "prompt engineers," guiding users in interacting with AI systems to maximize their benefits.
• Trust and Human Interaction: Despite AI's growing role, human judgment, empathy, and the ability to ask nuanced questions will remain essential.
• Promoting Digital Literacy: Librarians must continue promoting digital literacy, ensuring users can critically engage with AI tools.

Key Takeaways

• Libraries as Leaders in AI Ethics: Libraries uniquely promote ethical AI practices and ensure that marginalized voices are not left out of AI developments.
• Strategic Collaboration: Collaboration among libraries, tech companies, and users is critical to developing effective AI systems.
• Empowerment through AI: AI should be seen as a tool to enhance library services, but its implementation must be carefully managed to avoid reinforcing biases or creating new inequalities.

AI and Libraries: Current and Future Context

Andrew Cox leads the first presentation and discusses libraries' strategic responses to AI. He outlines several critical aspects of AI that libraries need to consider:

• Traditional Uses of AI: AI is already used in spam filtering, transcription, translation, and recommendation systems. While helpful, these applications come with issues of bias, especially concerning non-English-speaking users and minorities.
• Descriptive AI: This allows libraries to improve search capabilities through voice and image recognition technologies, which can help users navigate vast collections more effectively. However, if designed carefully, these systems could be better and reinforce existing biases.
• Generative AI: Tools like ChatGPT, which can generate content, code, and answer questions, present exciting possibilities and challenges. Libraries must know its limitations—such as misinformation, hallucinated content, biased results, and fabricated citations.

Strategic Frameworks for AI in Libraries

Andrew Cox discusses how libraries might develop strategic responses to AI:

• Proof of Concept Projects: Many libraries are already experimenting with small AI projects to explore its potential.
• Collaboration: Libraries should collaborate with technology providers to improve AI's role in library functions.
• Engaging Users: Libraries should engage with users to understand how they are utilizing AI and support their needs accordingly.
• Staff Training: Encouraging library staff to experiment with AI tools helps develop their skills and promotes innovative use cases.
• Strategy Development: Cox advocates for libraries to define strategic goals related to AI, such as improving services or creating a vision for AI aligning with library values.

Libraries as AI Intermediaries

Eric Borenstein presents the Netherlands AI Coalition, a national initiative that brings together cultural institutions like museums, libraries, and archives to collaborate on AI projects. He highlights the importance of community-driven projects like the "AI Parade," a traveling AI exhibition that engages the public and raises awareness about AI's role in society. The AI Parade has been highly successful, reaching over 1.5 million people quickly, showing that AI awareness is a critical educational focus for libraries.

Ethical Concerns and Responsible AI

The discussion turns to the ethical use of AI. Andrew and Eric stress that AI systems must be transparent, safe, and human-centered. They acknowledge the challenges related to bias, misinformation, and privacy concerns. Ethical frameworks must guide AI's implementation, ensuring it benefits library users while maintaining inclusivity and accessibility.

Decolonizing Libraries and Archives: Fostering Indigenous Knowledge and the Role of AI

Decolonizing Libraries and Archives: Fostering Indigenous Knowledge and the Role of AI

The discussion between Alexia Hudson-Ward and Jordan T. Clark is a thought-provoking continuation of their earlier dialogue on decolonization efforts in libraries. It focuses on collection practices, archival work, and the increasing role of artificial intelligence (AI) in academic settings. This conversation addresses how libraries can actively decolonize their spaces, collections, and practices while navigating the challenges of emerging AI technologies.

Decolonizing Libraries: Where to Begin

Clark opens the discussion by tackling one of the most frequent questions he receives: "What books should I buy to decolonize my library?" He emphasizes that decolonizing libraries is not merely about acquiring a checklist of books written by Indigenous authors but about a holistic, transformative mindset shift that inspires and motivates every aspect of library practice.

Key Takeaways:

1. Beyond Book Collections: While having literature from Native voices is essential, libraries must also physically and conceptually transform their spaces. Clark recounts an example where a librarian regularly rearranged the library to challenge students to engage with non-Western perspectives through rotating art pieces, quotes, and visual stimuli. This active engagement creates a dynamic learning environment.
2. Engagement Over Passivity: Libraries should not be passive repositories but active spaces inviting users to engage critically with their collections. For example, the librarian can reach the entire student body in high school libraries, making the library an interactive classroom.
3. Inclusive Spaces: Clark highlights Julie Fiveash's work at Harvard's Tozzer Library, which goes beyond books to include zines and other non-traditional forms of knowledge. This promotes the idea that libraries can be reimagined to offer more inclusive, non-Western perspectives.

Archives and Special Collections: Addressing Colonial Legacies

The conversation then shifts to archives and special collections, traditionally dominated by Western narratives. Clark discusses the importance of rethinking how libraries approach these collections, especially when dealing with materials historically used to oppress Indigenous communities.

Challenges and Opportunities in Archival Work:

1. The John Eliot Bible: Clark brings up the example of Harvard's Houghton Library, which houses the John Eliot Bible—the first Bible printed in the Western Hemisphere, but in the Wampanoag language, used to convert Indigenous people to Christianity forcibly. Rather than solely allowing this object to reflect a colonial narrative, it should be used to tell a fuller, more nuanced story, including its role in the modern-day Wampanoag language revitalization project.
2. Holistic Storytelling: Archivists should focus on telling the entire story of these objects, not just their colonial history. Centering Indigenous voices, such as the work of Jesse Little Doe Baird in language revitalization, helps shift the narrative away from colonial oppression toward empowerment and cultural survival.
3. Partnerships and Networks: Libraries must build coalitions with Native communities and other institutions to foster an inclusive network that elevates marginalized voices. Sharing knowledge across institutions allows for a broader understanding of how to approach decolonizing archives and special collections.

Decolonizing AI: Challenges and Cautions

Hudson-Ward and Clark then explore how artificial intelligence is both a tool for advancing Indigenous knowledge and a source of concern, as it can perpetuate existing biases rooted in colonialism.

AI in Academia and Indigenous Knowledge:

1. AI's Dependence on Existing Data: Large language models (LLMs) like ChatGPT do not create knowledge; they scrape information from existing sources. Clark notes that AI will only perpetuate those biases if the scraped data is colonized or biased. This is particularly concerning for Native communities whose knowledge has historically been stolen or misrepresented.
2. Amplifying Erasure: AI systems trained on colonized data sources could unintentionally amplify the erasure of Native voices rather than uplift them. The danger lies in reinforcing problematic narratives if Indigenous perspectives are separate from the foundational data these systems learn from.
3. Representation in AI Development: Indigenous voices must be included in developing and deploying AI technologies to combat this. Clark stresses that without Native representation in AI development, these technologies are unlikely to advance Indigenous knowledge effectively.

Building Trust and Relationships with Indigenous Communities

One of the central themes in the discussion is the necessity of building trust between institutions and Native communities. This trust is vital for fostering knowledge sharing and ensuring that Indigenous knowledge is not appropriated or misused.

Recommendations for Libraries and Academic Institutions:

1. Fostering Long-Term Relationships: Clark underscores that libraries must build consistent, trustworthy relationships with Native communities. This will allow Indigenous voices to be centered on collecting, preserving, and disseminating knowledge.
2. Representation Matters: Institutions must ensure that Indigenous people are actively involved in decision-making processes, whether in hiring Native staff, curating collections, or creating AI tools. Representation within institutions is essential for meaningful progress.
3. Mutual Benefit: For Native communities to share their knowledge, institutions must provide reciprocal benefits, such as increased educational opportunities for Native students or collaborative partnerships that benefit the community.

AI, Cultural Heritage, and the Future

The conversation concludes with reflections on how AI technologies can be both a challenge and an opportunity for Indigenous communities. Hudson-Ward mentions how African nations call for AI tools that better represent their cultural artifacts and languages, which mirrors the need for Native American representation in AI systems.

Balancing AI's Benefits and Risks:

1. Developing Culturally Relevant AI Tools: Clark emphasizes the need for AI tools to process Indigenous languages and cultural artifacts accurately. Without such developments, AI risks perpetuating stereotypes or misrepresenting Indigenous knowledge.
2. Collaborative AI Development: Universities and tech companies must collaborate with Indigenous communities to build AI systems that reflect their cultural and historical realities. This will help avoid the pitfalls of technology that misinterprets or distorts Indigenous knowledge.

Moving Forward with Intentionality

In this engaging conversation, Clark and Hudson-Ward offer valuable insights into how libraries and archives can better support Indigenous communities by decolonizing their practices and spaces. Whether through rethinking how collections are curated, building trust with Native communities, or ensuring Indigenous representation in AI development, libraries can take multiple paths to foster a more inclusive environment.

The conversation highlights that decolonization is not a checklist but a mindset and practice that must permeate every layer of an institution—from book collections to AI systems. By focusing on building relationships, fostering inclusivity, and using technology responsibly, libraries and academic institutions can begin to reverse colonialism's legacy and create spaces where Indigenous knowledge is truly valued.