Day archives: February 24th, 2026

What the Science Says About Hallucinations in Legal Research

Over the past three years, researchers have published dozens of studies examining exactly when and why AI fails at legal tasks—and the patterns are becoming clearer. The research is clear: AI hallucinations in legal work are real, measurable, and follow predictable patterns. Rebecca Fordon evaluates the data and research that documents six critical patterns lawyers must understand to make sound, actionable and effective decisions about using AI.

Subjects: AI, KM, Legal Research, Legal Research Training, Legal Technology

AI Under the Hood

Knowing the difference between a general AI tool and one trained on specific sources can mean the difference between getting an accurate answer and becoming quickly frustrated with outcomes that either don’t answer the question thoroughly or answer the question in a confused mixture of fact and fiction. While not always clear, the data that lies behind the GenAI tool is just as important to consider as the user interface or the cost. Without trustworthy or relevant underlying information, the resulting AI-generated output will be less helpful or less trusted and result in inefficiencies as lawyers and staff work to fill the gaps in the GenAI’s response. Considering these factors, Kristopher Turner’s article identifies how and why a retrieval augmented generation (RAG) can give focused and highly specific answers related to one area that someone needs to quickly understand.

Subjects: AI, KM, Legal Education, Legal Profession, Legal Research, Legal Technology, Search Engines

The greatest risk of AI in higher education isn’t cheating – it’s the erosion of learning itself

Over the past eight years Nir Eisikovits and Jacob Burley have been studying the moral implications of pervasive engagement with AI as part of a joint research project between the Applied Ethics Center at UMass Boston and the Institute for Ethics and Emerging Technologies. In a recent white paper, we argue that as AI systems become more autonomous, the ethical stakes of AI use in higher ed rise, as do its potential consequences.

Subjects: AI, Education, Ethics, KM