“Even if they can be tuned to be right more of the time, they will still have failure modes — and likely the failures will be in the cases where it’s harder for a person reading the text to notice, because they are more obscure.”

– Dr. Emily Bender, linguistics scholar, on AI’s hallucination problem

The rapid spread of AI-generated content poses a significant threat to public trust and the overall quality of information sources, with potentially serious real-world consequences across various fields such as medicine, government, law, and education. A major issue is AI hallucination–an instance where an AI model generates misleading, inaccurate, or entirely fabricated content, often without a clear basis in its training data. While some of these errors are easy to spot, others are subtle and difficult to detect, making them potentially dangerous. Even when AI generated information is cited, a recent study found that leading AI chatbots incorrectly cite their sources 60% of the time. Another related concern is AI slop—a flood of low-quality, often misleading, AI-generated content overwhelming the internet and social media. As AI continues to shape the way information is produced and consumed, students need to understand the nuances of these issues and learn how and when to fact check the information they get from AI.