Unmasking artificial intelligence (AI): Identifying articles written by AI models

Abstract

The rise of linguistic models as part of artificial intelligence (AI) in academic writing has brought both benefits and challenges. While AI can generate content that closely resembles human writing, recognizing AI-generated content is difficult due to its lack of obvious errors, prompt-based adaptability to various styles, broad subject range, and rapid production speed. To address this issue, various methods, such as technical analysis, metadata examination, stylometric analysis, tests for coherence, and AI detection models like GPTZero, have been developed. Ethical concerns include the risk of duplicity, writing validity, responsibility, and authorship credit. The future of AI-generated content identification is expected to involve improvements in AI detection algorithms, deep analytic tools, interdisciplinary cooperation, and ethical guidelines.