  The widespread adoption of Large Language Models and publicly available
ChatGPT has marked a significant turning point in the integration of Artificial
Intelligence into people's everyday lives. The academic community has taken
notice of these technological advancements and has expressed concerns regarding
the difficulty of discriminating between what is real and what is artificially
generated. Thus, researchers have been working on developing effective systems
to identify machine-generated text. In this study, we utilize the GPT-3 model
to generate scientific paper abstracts through Artificial Intelligence and
explore various text representation methods when combined with Machine Learning
models with the aim of identifying machine-written text. We analyze the models'
performance and address several research questions that rise during the
analysis of the results. By conducting this research, we shed light on the
capabilities and limitations of Artificial Intelligence generated text.
