Reflective Thinking and Attitudes Toward Academic Requirements In AI-Supported Graduate Education

Artificial intelligence (AI) tools are increasingly being integrated into higher education, transforming how graduate students perform academic tasks such as research, writing, and problem solving. While AI enhances efficiency and productivity, concerns remain regarding its influence on reflective thinking, academic rigor and scholarly integrity. This study examined reflective thinking and attitudes toward academic requirements in AI-supported graduate education using a convergent-parallel mixed-methods design. In the quantitative phase, 51 graduate students (30 master’s and 21 PhD students) completed a structured survey that measured reflective thinking, attitudes toward academic requirements, and AI engagement. Data were analyzed using descriptive statistics, Pearson’s correlation, and independent samples t-tests. In the qualitative phase, semi-structured interviews were conducted with 14 participants to explore their experiences with AI tools in academic tasks. The findings revealed very high levels of reflective thinking, positive attitudes toward academic requirements, and strong engagement with AI among graduate students. Significant positive relationships were found among reflective thinking, attitudes, and AI engagement, indicating that these variables are closely interconnected in AI-supported learning environments. No significant differences were observed between master’s and PhD students. Qualitative results showed that students critically evaluated AI-generated responses, verified information using credible academic sources, and maintained awareness of academic integrity when using AI tools. Overall, the study concluded that AI can support graduate learning without diminishing reflective thinking when used responsibly, and highlighted the importance of promoting ethical AI use, critical evaluation skills, and reflective learning practices in graduate education.