“This library technology study examines IT budget sufficiency, capital funding, technology refresh cycles, upgrade delays, collaboration with academic departments, strategic alignment, generative-AI subscription spending, cloud and AI spending, and technology budget composition.
The report finds that library IT budgets are only moderately sufficient. 12.12% say their current library IT budget is sufficient, while 42.42% call it moderately sufficient. One-third, 33.33%, say it is insufficient, though no respondent calls it critically insufficient.
Capital funding for equipment replacement is usually irregular. 60.61% receive capital funding irregularly, and 6.06% never receive it. Only 18.18% receive such funding annually, and 3.03% every two to three years.
Articles Posted in Library Professional Development
When Humanlike Robots Make Mistakes: Social Expressiveness and the Fragility of Trust
Introduction
A recent Tech Xplore article reports on research suggesting that making humanoid robots more socially expressive (through eye contact, gestures, nodding, and other humanlike behaviors) can increase engagement but may also carry an unexpected cost: when an expressive robot makes a mistake, people may react to the error more as a social violation than as a simple technical failure.
The article, “A Humanoid Robot’s Social Expressiveness May Backfire When It Makes Mistakes,” was written by Ingrid Fadelli and published by Tech Xplore/Phys.org on August 28, 2026, with editing by Robert Egan. It reports on research conducted principally by investigators at Drexel University and published in Science Robotics. The underlying study, by Yigit Topoglu and colleagues, is titled “Multilevel Dynamics of the Brain, Hormones, Mind, and Behavior in Social Human-Robot Interaction.” The following is an overview of the two articles:
Bill Gates on the Turbulent AI Era: Promise, Risk, and the Choices Ahead
Overview
In his August 26, 2026 essay, “The Turbulent AI Era Is Here. The Choices We Make Now Are Critical,” Bill Gates presents artificial intelligence as a technological transition potentially different in important respects from earlier waves of automation. AI can increasingly perform cognitive work, can spread rapidly through technologies and infrastructure already in widespread use, and can be accessed through ordinary language without requiring users to master specialized computer skills. Gates is not arguing against AI. On the contrary, he remains strongly optimistic about its potential in medicine, education, agriculture, scientific research, government services, clean energy, and assistance for people who otherwise lack access to specialized expertise. His concern is that these benefits will not necessarily be distributed fairly and that significant social and economic disruption could accompany them.
Gates identifies three major categories of concern:
Understanding World Models: An Emerging Direction in Artificial Intelligence and Its Potential Significance for Legal Research
David G. Badertscher
“Every generation of legal researchers inherits new tools. Their enduring responsibility is to learn how to use them wisely.”
Introduction
Understanding Budget Reconciliation: How Congress Fast Tracks Major Fiscal Legislation
Budget reconciliation is a special congressional procedure created by the Congressional Budget Act of 1974 that allows Congress to consider legislation affecting federal spending, revenues (taxes), and the debt limit under expedited procedures. Most notably, reconciliation bills can pass the Senate with a simple majority vote rather than the 60 votes normally needed to overcome a filibuster. As a result, reconciliation has become one of the most important tools for enacting major fiscal policy changes. The following is an overview of the congressional budget reconciliation process and a discussion of its importance to librarians, researchers, and the general public.
What Is Reconciliation?
Reconciliation is designed to align existing laws with the fiscal goals established in a congressional budget resolution. It can be used to:
Interpretability and Retrieval Augmented Generation (RAG): How They are Reshaping AI Legal Research
Artificial intelligence is now woven into the daily fabric of legal work. From case law research to contract analysis and compliance monitoring, AI systems are accelerating tasks that once required hours of manual review. But as these tools become more capable, the legal profession faces a central challenge: How can lawyers trust AI in high‑stakes environments where accuracy, transparency, and defensibility are non‑negotiable?
Two concepts have emerged as foundational to answering that question: interpretability and retrieval-augmented generation (RAG). While distinct, they work together to create AI systems that are transparent, grounded in evidence, and aligned with professional legal standards. Although both have existed for some time, their integration into legal research remains in its infancy, and there is much to learn. This post explores how these systems are reshaping AI legal research based on a review of current industry sources.
Understanding Interpretability in Legal AI
American Association of Law Libraries (AALL): The Education Edge
Welcome to The Education Edge—the new name and refreshed look of what was formerly the [AALL] Education Update. Designed to keep you learning and moving forward, The Education Edge highlights timely resources, ideas, and opportunities to support your professional growth.
Explore resources of The Education Edge.
Overview of Two VERDICT Columns by Marci A. Hamilton on the Epstein Files*
Two recent opinion columns published on Justia Verdict – Legal Analysis and Commentary from Justia examine the legal, political, and moral implications of the continuing disclosures surrounding the Jeffrey Epstein investigations. Written by Professor Marci A. Hamilton of the University of Pennsylvania and founder of CHILD USA, the essays present a forceful argument that accountability for systemic abuse requires sustained legal pressure and public transparency. The views expressed are those of the author and do not represent the official position of Justia.
1. “The Three Avenues to Justice in the Epstein Cases” (Feb. 24, 2026)
In The Three Avenues to Justice in the Epstein Cases, Professor Hamilton argues that meaningful accountability is likely to emerge through three principal legal pathways rather than through federal prosecutorial initiative alone.
Criminal Law Library Blog

