Awareness, Perceptions, and Readiness for Artificial Intelligence (AI) Among Graduate Students

Artificial intelligence (AI) has made significant contributions to various fields, including education, healthcare, business, and industry. By utilizing machine learning, data analysis, and automation, AI enhances processes and fosters innovations. This study explored graduate students’ awareness, perceptions, readiness, and challenges regarding the use of AI in academic settings. Employing a quantitative descriptive-correlational research design, data were gathered from 25 graduate students through a structured questionnaire using a four-point Likert scale. Descriptive statistics, Pearson’s correlation, and multiple regression analyses were used to analyze the data. The findings indicated that graduate students exhibited very high levels of awareness (M = 3.73), perception (M = 3.54), and readiness (M = 3.38), suggesting that they were well-informed, held positive attitudes, and were prepared to incorporate AI into academic tasks. However, a notable level of challenges (M = 2.92) was also identified, particularly regarding data privacy, ethical considerations, limited institutional support, and technical constraints. Correlation analysis revealed significant relationships among awareness, perceptions, and readiness, with awareness showing a strong positive relationship with readiness (r = 0.760, p < 0.05). Furthermore, regression analysis suggested that awareness significantly predicted readiness, whereas perceptions did not have a statistically significant effect. The study concludes that awareness is a crucial factor in enhancing students’ readiness to use AI, highlighting the importance of strengthening AI literacy in higher education institutions. The findings suggest that institutions should consider implementing training programs, integrating AI into the curriculum, and establishing clear ethical guidelines to support responsible AI use. Additionally, enhancing institutional support and infrastructure would be beneficial in addressing the existing challenges and ensuring effective AI integration.