V3I1P54

Advances in AI-Generated Text Detection: A Systematic Review

Dr. S. V. Viraktamath¹, Mahmad Sohel²*, Bhogeshrao³, Anita Mallangoudra⁴ 

Abstract

Advanced language models can now create writing that looks very similar to human text, making it harder to know whether a message, review, essay, or report was made by a person or a machine. This rapid growth of AI-generated text brings new chances to improve learning, research, and communication, but it also raises concerns about honesty, misinformation, and content quality. Recent studies show that modern models can write convincing summaries, scientific abstracts, and creative work, while detection tools often struggle to separate real and artificial content, especially when the text is slightly changed or produced with new generation methods. Research has also explored how machine written images, reviews, stories, and mathematical reasoning can affect trust in different fields. As AI systems continue to improve, many existing detection methods become less effective, highlighting the need for stronger, more reliable approaches. This work brings together key findings from recent research to help understand how AI-generated text is created, why it is difficult to identify, and why improved detection frameworks are necessary for safe and responsible use of advanced technologies. and flywheel maintain stability in self-balancing bicycles during tilting. Autonomous capability includes use of various sensors and camera to detect objects for navigation and use computer vision efficiently with the help of machine learning algorithms. The proposed design framework will help in a transformative era of innovation in urban mobility, promising safer and more sustainable ways of getting around in cities.

Keywords:

AI Text Detection, Machine Learning Classifiers, Large language models (LLMs), Deep Learning Models, Bidirectional encoder representations from transformers (BERT), Support Vector Machine (NL), Text Authenticity, Academic Integrity, Plagiarism Check, Linguistic Features, Natural Language Processing (NLP)