This paper investigates the term "AI hallucination" and its usage in the field of artificial intelligence. The authors conducted a systematic review of 14 databases to identify and analyze definitions of "AI hallucination" across various domains. They found that the term is not consistently defined, and different researchers have used it in different ways. Some definitions focus on errors in text generation, while others refer to the generation of false information. The authors also note that the term "hallucination" is being used in a way that may be misleading, as it is associated with mental illness, which could have negative implications for AI research and development. The authors argue that the term "hallucination" is not appropriate for describing errors in AI systems, as AI does not experience hallucinations in the same way humans do. Instead, errors in AI systems are often due to data and prompts, not the absence of stimuli. The authors also highlight the need for a more precise and universally accepted definition of "AI hallucination" to ensure clarity and consistency in research and development. They suggest that alternative terms, such as "fact fabrication" or "stochastic parroting," may be more appropriate for describing certain types of errors in AI systems. The authors conclude that the term "hallucination" is not well-defined and that there is a need for a more precise and universally accepted definition of "AI hallucination" to ensure clarity and consistency in research and development. They also suggest that alternative terms may be more appropriate for describing certain types of errors in AI systems. The authors emphasize the importance of developing a consistent and accurate terminology for AI systems to ensure that the term "hallucination" is used appropriately and that the potential risks and benefits of AI systems are properly understood.This paper investigates the term "AI hallucination" and its usage in the field of artificial intelligence. The authors conducted a systematic review of 14 databases to identify and analyze definitions of "AI hallucination" across various domains. They found that the term is not consistently defined, and different researchers have used it in different ways. Some definitions focus on errors in text generation, while others refer to the generation of false information. The authors also note that the term "hallucination" is being used in a way that may be misleading, as it is associated with mental illness, which could have negative implications for AI research and development. The authors argue that the term "hallucination" is not appropriate for describing errors in AI systems, as AI does not experience hallucinations in the same way humans do. Instead, errors in AI systems are often due to data and prompts, not the absence of stimuli. The authors also highlight the need for a more precise and universally accepted definition of "AI hallucination" to ensure clarity and consistency in research and development. They suggest that alternative terms, such as "fact fabrication" or "stochastic parroting," may be more appropriate for describing certain types of errors in AI systems. The authors conclude that the term "hallucination" is not well-defined and that there is a need for a more precise and universally accepted definition of "AI hallucination" to ensure clarity and consistency in research and development. They also suggest that alternative terms may be more appropriate for describing certain types of errors in AI systems. The authors emphasize the importance of developing a consistent and accurate terminology for AI systems to ensure that the term "hallucination" is used appropriately and that the potential risks and benefits of AI systems are properly understood.