AI hallucinations occur when Generative AI tools produce incorrect, misleading, or nonexistent content. Content may include facts, citations to sources, code, historical events, and other real-world information. Remember that large language models, or LLMs, are trained on massive amounts of data to find patterns; they, in turn, use these patterns to predict words and then generate new content. The fabricated content is presented as though it is factual, which can make AI hallucinations difficult to identify. A common AI hallucination in higher education happens when users prompt text tools like ChatGPT or Gemini to cite references or peer-reviewed sources. These tools scrape data that exists on this topic and create new titles, authors, and content that do not actually exist.
Image-based and sound-based AI is also susceptible to hallucination. Instead of putting together words that shouldn’t be together, generative AI adds pixels in a way that may not reflect the object that it’s trying to depict. This is why image generation tools add fingers to hands. The model can see that fingers have a particular pattern, but the generator does not understand the anatomy of a hand. Similarly, sound-based AI may add audible noise because it first adds pixels to a spectrogram, then takes that visualization and tries to translate it back into a smooth waveform.