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.