The rapid advancement of digital technologies has significantly impacted academic practices, particularly in the area of plagiarism detection. As universities and research institutions adopt tools to safeguard academic integrity, concerns arise about their effectiveness and potential limitations. This study investigates the role of automated plagiarism detection tools in higher education, examining how they influence academic practices and the detection of both traditional and AI-generated plagiarism. Despite the sophistication of tools like Turnitin, PlagScan, GPTZero, and QuillBot, the research finds that these systems often struggle with accurately interpreting context, resulting in false positives and overlooked instances of plagiarism. The study underscores the necessity of combining technology with human judgment, recognizing that such tools should be seen as supplementary rather than definitive measures of originality. Grounded in theoretical frameworks such as Technological Determinism, Actor-Network Theory (ANT), and Socio-Technical Systems Theory (STS), the research highlights the complex relationship between technology, academia, and societal expectations. Through a qualitative analysis of existing literature, the study identifies key challenges and suggests that hybrid approaches, blending technological tools with human oversight, may offer a more balanced and effective approach to plagiarism detection. The findings encourage further exploration into the ethical implications of reliance on automated systems in education and their broader impact on academic integrity.