As Artificial Intelligence (AI) becomes increasingly embedded in higher education, concerns surrounding Responsible Artificial Intelligence (RAI), particularly trust and transparency have moved from abstract ethical discussions to practical institutional challenges. While existing research has largely focused on technological capabilities and learning outcomes, empirical evidence on university students’ perceptions of Responsible AI remains limited, especially in developing country contexts. This study investigates university students’ awareness, ethical perceptions, perceived risks, and trust related to AI usage in higher education. A quantitative, cross-sectional survey was conducted among 719 undergraduate students from multiple academic disciplines at a Sri Lankan university. Data were analyzed using reliability testing, exploratory factor analysis (EFA), and multiple linear regression to examine the relationships between AI awareness, ethical perceptions, perceived risks, and trust in AI systems. The findings indicate that students demonstrate moderately high awareness of AI-related ethical issues, including bias, transparency, and data privacy concerns. While AI tools are widely used for academic support activities such as content development, coding, and proofreading, students exhibit cautious trust, often validating AI-generated outputs before use. Regression analysis reveals that AI awareness and ethical perceptions are significant positive predictors of trust in AI, whereas perceived risks negatively influence trust. The results further show strong student support for institutional AI governance, including clear usage guidelines, disclosure requirements, and responsible AI policies. These findings highlight that trust in AI is shaped not merely by usage frequency but by students’ ethical understanding and risk awareness. This study contributes empirical evidence to the growing literature on Responsible AI in education and offers practical insights for universities and policymakers seeking to design transparent, ethical, and student-centered AI governance frameworks in higher education.