Exploring the Attitudes of Postgraduate Students toward Artificial Intelligence in Academic Context: A Cognitive–Affective–Behavioral Analysis and Implications for Modern Education

Abstract

This study examines postgraduate students’ attitudes toward Artificial Intelligence (AI) in academic contexts through a cognitive–affective–behavioural framework. With the rapid integration of AI in higher education, understanding how students perceive and engage with these technologies has become increasingly important. The study is grounded in the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM), which explain how knowledge and perceptions influence attitudes and subsequent behaviour. A quantitative research design was adopted, and data were collected from postgraduate students using a structured questionnaire with sample of 303. The analysis included descriptive statistics, one-way ANOVA, correlation, and regression techniques. The findings revealed that students generally exhibit moderately positive attitudes toward AI across cognitive, affective, and behavioural dimensions with mean ranging from 3.95,3.65 and 3.61. The ANOVA results indicated no significant differences in attitudes across social categories, suggesting relatively consistence attitude among students. Further analysis showed significant positive relationships between cognitive, affective, and behavioural components, confirming that students’ knowledge of AI influences their attitudes and usage patterns. Regression results demonstrated that cognitive and affective factors significantly predict behavioural engagement with AI, highlighting the importance of awareness and perception in shaping technology use. The study enhances understanding by using a CAB approach—looking at students’ thoughts, feelings, and actions together—to better explain their attitudes toward AI, unlike models like the Technology Acceptance Model and Theory of Planned Behavior, which focus on fewer aspects rather than all three aspects contributing to the growing literature on AI in education.It also offers important implications for policymakers and educators in promoting inclusive and effective AI-integrated learning environments.