MiALA 2026 Annual Conference

Academic librarians with high autonomy generally view it as a key benefit of their role. For new librarians moving from a structured LIS program to an autonomous academic role, adjusting to this independent reality takes time. In this session, early career academic librarians will discuss their experiences with autonomy in their positions and provide ideas for pushing through the discomfort and growing into your autonomy as an academic librarian.

Learning Objective(s): Attendees will be able to 1) Build a sense of validation among early career librarians with different experiences of autonomy in their roles; 2) Identify opportunities and challenges that post-Covid hybrid work environments have created for academic librarian autonomy; and 3) Develop strategies to be productive and fulfilled in librarianship roles.

This panel will present the perspectives of four librarians developing different types of credit-bearing information literacy instruction for a variety of disciplines, as well as their department head providing an administrative perspective. Each librarian will describe the origin of their project, how it has evolved, and their personalized approach to developing credit-bearing instruction to support the faculty and students in various departments.

Learning Objective(s): 1) Participants will be able to describe several approaches to developing credit-bearing courses and identify opportunities in their teaching/libraries for similar projects; 2) Participants will be able to assess if these approaches are feasible in their libraries; and 3) Participants will be able to devise their own approaches to move toward designing and advocating for teaching information literacy in credit-bearing courses.

This presentation explores the TIES Center's ten dimensions of belonging: Present, Invited, Welcomed, Known, Accepted, Involved, Supported, Heard, Befriended, and Needed, providing a foundation to advance campus belonging initiatives. This presentation provides an introduction to belonging in higher education before going through the ten dimensions, connecting them to library work. With this inspiration jumpstart, participants will reflect and brainstorm applications to their own work. Sharing ideas with fellow attendees will foster connections and reinforce learning.

Learning Objective(s): Attendees of this session will 1) Understand belonging work within the context of higher education; 2) Name the ten dimensions of belonging; and 3) Connect dimensions of belonging to their work.

Academic librarians support course materials through OER, course guides, and reserves. This lightning talk shares a case study in which a newly hired instructor partnered with library staff to create a zero-cost, multimedia set of course resources. By combining open textbooks, library ebooks, podcasts, and expert video content, the collaboration supported Universal Design for Learning, broadened perspectives, and improved access while reducing student costs.

Learning Objective(s): 1) Describe the pedagogical value of incorporating podcasts and video alongside traditional course readings; 2) Explain how multimodal resources support Universal Design for Learning and undergraduate engagement; and 3) Apply a scale-able approach for expanding course materials beyond a single-textbook model while reducing student costs.

This lightning talk introduces the AI Literacy Compass—a values-first, harm-aware framework for working with AI instruction as a librarian. Beginning at True North (learning, equity, integrity, environment, labor), then moving through literacy, boundaries, and practice, the session addresses complex librarian and campus attitudes, from principled refusal to uncritical, shortcut-focused use. Attendees will leave with language and decision tools to support critical, ethically grounded engagement in their own AI-related work.

Learning Objective(s): Articulate a values-first definition of AI literacy that supports both critical use and principled refusal of generative AI tools

The use of AI tools in evidence synthesis is expanding rapidly, and librarians are frequently asked by researchers for guidance on selecting the best AI tools to enhance their work. This session will introduce some of the available AI tools and where they fit in the evidence synthesis process, resources for critically appraising their use, and the possibilities and potential pitfalls of integrating AI tools.

Learning Objective(s): 1) Participants will identify how artificial intelligence could be used in evidence synthesis; 2) Participants will evaluate AI tools for use in evidence synthesis to determine effectiveness and use in their own research; and 3) Participants will critically appraise AI tools for use in evidence synthesis.

Librarians at Central Michigan University (CMU) Libraries and CMU’s Covenant HealthCare College of Medicine collaborated to summarize, categorize, and publicize the evidence synthesis and systematic review services available to CMU students, faculty, residents and medical education partners, resulting in the creation of a 3-tier service model and accompanying LibGuide. In addition to informing patrons, this formalization allows librarians to proactively set clear boundaries around a growing service to help prevent current and future burnout.

Learning Objective(s): ​​​​1) Evaluate 3-tier model for evidence synthesis support, including request form for advanced support; 2) Learn how librarians with little/no systematic review experience/in non-health areas can still support constituents? evidence synthesis efforts; 3) Understand how formalizations of services can help librarians manage expectations, minimize stress, and prevent burnout; and 4) Apply a similar model of evidence synthesis support at attendees’ home institutions.

In an effort to fully represent the research output of our faculty in all departments equally we examined how they are currently being represented within various bibliometric data reporting systems. In this session we will explain why we chose to do this project, how we outlined this work, what challenges we encountered and how we overcame them. We will also talk about what we learned from the experience and our plans for continuing this work.

Learning Objective(s): 1) Attendees will gain an understanding of the reporting capabilities of various scholarly output reporting systems; and 2) Attendees will learn about one way to organize and implement an assessment program to examine the way their faculty output is being reported. .

As early adopters of SpringShare’s LibGuides, Central Michigan University librarians have been creating guides for nearly two decades. To combat the resulting unwieldy collection of over 400 guides, four librarians formed an ad hoc committee to administer the Streamlining LibGuides Project. While the project made significant headway, it also encountered notable resistance and delays. Presenters will highlight project phases and pitfalls as well as best practices and practical recommendations for others considering similar LibGuides projects.

Learning Objective(s): ​​​​1) Learn how one university attempted to streamline their LibGuides collection while managing tension between UX and academic freedom; 2) Discover potential pitfalls of a LibGuides streamlining project; 3) Evaluate a non-prescriptive best practice guide for streamlining LibGuides; and 4) Consider practical recommendations for undertaking a similar LibGuides project.

As AI becomes increasingly embedded in library tools and research support, librarians face growing tension between ethical concerns, environmental impact, and student needs. This panel brings together four librarians with varying relationships to AI, examining how “AI fatigue” shapes daily practice and institutional decision making. Panelists will explore ethical lenses, societal pressures, and approaches to engagement, inviting participants into reflective discussion about their own “True North” in teaching and supporting critical AI use.

Learning Objective(s): ​​​​1) Articulate how AI fatigue manifests in library work, including emotional, ethical, environmental and labor related dimensions; 2) Analyze how institutional, economic, and societal pressures shape librarians’ expectations to adapt, resist, or mediate AI technologies; and 3) Reflect on their own positionality and values when supporting student AI use within higher ed and library contexts.