<![CDATA[This research is motivated by the rapid integration of Artificial Intelligence (AI) within the academic ecosystem, which offers research efficiency but simultaneously triggers risks of cognitive degradation and scientific ethical crises. This study aims to evaluate the role of AI as a cognitive supporting tool and analyze the challenges to the authenticity of thinking across all stages of the scientific method. The study employs a multi-methodology approach integrating five systematic research methods: a Systematic Literature Review (SLR), explanatory quantitative surveys, comparative qualitative content analysis assisted by Natural Language Processing (NLP) semantic algorithms, controlled computational simulation experiments, and an explorative phenomenological case study. The results indicate that AI provenly functions as an effective supporting tool capable of exponentially reducing research bureaucracy and expanding big data analysis capabilities. However, empirical testing also confirms substantial risks, including cognitive offloading, disguised plagiarism, and the threat of pseudo-correlation due to the "black box" nature of advanced algorithms. In conclusion, AI shifts the scientific method from a traditional linear approach into an exponential data-driven cycle. Consequently, higher education institutions are recommended to immediately formulate strict AI governance regulations while maintaining the "human-in-the-loop" principle to preserve intellectual integrity.]]>
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