Information Literacy

August 31, 2026

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:

August 20, 2026

Learning How to Think Like a Lawyer in the Age of AI: Complementary Advice for Incoming Law Students

by David Badertscher

Introduction

Two distinguished law professors recently offered incoming law students advice from different but unusually complementary perspectives. Vikram David Amar, writing broadly about the habits of mind and disciplined effort required to succeed in law school, is principally concerned with how students learn to think like lawyers. Michael C. Dorf, focusing more specifically on artificial intelligence, considers how that learning process can be preserved and adapted at a time when AI systems can produce seemingly authoritative answers almost instantly.

Considering their essays together reveals an important relationship that might be less apparent if each were read in isolation. The traditional methods through which law students develop legal reasoning, judgment, and intellectual independence are now encountering technologies capable of retrieving information, summarizing complex material, and generating plausible responses with remarkable speed. The question is therefore not simply whether students should use AI, but how they can take advantage of useful technological tools without allowing those tools to displace the intellectual work that legal education is intended to cultivate.

August 3, 2025

Should Librarians be Involved in Auditing Generative AI Systems for Factual Accuracy?

by David Badertscher

As generative artificial intelligence (GenAI) systems become increasingly integrated into search engines, legal research platforms, healthcare diagnostics, and educational tools, questions of factual accuracy and trustworthiness have come to the forefront. Erroneous or hallucinated outputs from large language models (LLMs) like ChatGPT, Gemini, and Claude can have serious consequences, especially when these tools are used in sensitive domains.

The sheer volume of information processed by AI systems makes comprehensive auditing a significant challenge. This necessitates finding efficient and effective strategies for human oversight. In this context, the question arises: Should librarians, especially those trained in research methodologies and information literacy, be involved in auditing these systems for factual accuracy? The answer is a resounding yes.

The Librarian’s Expertise in Information Validation