Call for paper: Deriving library metrics using Generative AI/LLMs

This special issue aims to investigate how LLMs and Generative AI metrics can be applied to the library sector, including all types of information handling, and how these metrics can be customised for more eﬀective library operations. It introduces library research on how AI is used in library evaluations and assessments. Its purpose is to help ensure library practitioners participate in applying AI (e.g., LLMs and GAI) to describe library value and provide data for improvement decision-making.

This special issue aims to address the following questions:

What LLM metrics are most eﬀective for assessing and evaluating library operations?
How can LLMs be effectively used in library sectors and domains? (benefits and opportunities)
What are the limitations and challenges in using LLM applications in library operations?

List of topic areas

AI-powered library chatbot systems, such as virtual assistants that handle patron questions and guide research in real-time text or voice.
Predictive analytics to make proactive decisions- using (recent) past data to forecast future library needs
Correlating Usage with Value- matching manpower and time spent on resources
Automated Metadata Creation and Usage Tracking – AI generate descriptive labels and logs data
AI for Electronic Resource Management -AI to monitor subscriptions, troubleshoot access issues, and optimise which e-resources to keep or drop
AI applications on Metadata -Applying AI to clean, enrich, or standardise metadata so collections are easier to search and organise
AI-assisted metrics for digital libraries/Digital Information Interactions -Using AI to measure how users engage with digital collections (clicks, reads, searches) to spot patterns
AI-based Library Usage Trend Forecasting -Predicting future borrowing, visit, or online activity patterns so the library can plan ahead.
Creating and Using Analytics Dashboards - Building visual displays that pull key statistics into one screen for quick, at-a-glance decision-making.
AI-assisted real-time library data visualization-Using AI to update live charts and maps of library activity as it happens, so you see current trends instantly
Automated Reporting and Data Cleansing -Letting AI generate routine reports and fix messy, duplicate, or incomplete data automatically.
Dynamic Library Resource Allocation -Shifting budgets, staff, or physical space on the fly based on real-time demand detected by AI
AI-Library Interfaces -The overall ways that AI tools and library systems connect and interact with each other and with users

Submissions information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/PMM

Author guidelines must be strictly followed. Please see: https://www.emerald.com/journals/author-guidance/1627/emerald-publishing-author-guidelines

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else while under review for this journal.