| Abstract: |
The rapid rise of generative AI tools such as ChatGPT has prompted significant shifts in how higher education institutions approach academic integrity. Many universities have implemented AI detection tools like Turnitin AI, GPTZero, Copyleaks, and ZeroGPT to identify AI-generated content in student work. This qualitative evidence synthesis draws on peer-reviewed journal articles published between 2021 and 2024 to evaluate the effectiveness, limitations, and ethical implications of AI detection tools in academic settings. While AI detectors offer scalable solutions, they frequently produce false positives and lack transparency, especially for multilingual or non-native English speakers. Ethical concerns surrounding surveillance, consent, and fairness are central to the discussion. The review also highlights gaps in institutional policies, inconsistent enforcement, and limited faculty training. It calls for a shift away from punitive approaches toward AI-integrated pedagogies that emphasize ethical use, student support, and inclusive assessment design. Emerging innovations such as watermarking and hybrid detection systems are discussed, though implementation challenges persist. Overall, the findings suggest that while AI detection tools play a role in preserving academic standards, institutions must adopt balanced, transparent, and student-centered strategies that align with evolving digital realities and uphold academic integrity without compromising rights or equity. [ABSTRACT FROM AUTHOR] |