Determinants of artificial intelligence and its effects on learning motivation among students in higher education institutions

Artificial intelligence (AI) is widely acknowledged for its capacity to revolutionise higher education by enabling more dynamic, personalised, and efficient learning environments. This study examines the influence of opportunities to utilise AI, the perceived significance of AI, and students’ attitudes towards AI on their motivation to learn. Data were collected from 208 students at Universiti Teknologi MARA (UiTM) Kelantan Branch using a quantitative cross-sectional survey design, based on a targeted sample size of 331. The findings reveal significant positive relationships between students’ attitudes, the perceived importance of AI, and their motivation to engage with it in their learning process. The opportunity to utilise AI also emerged as a notable factor in enhancing student motivation. Interpreted through the lens of Expectancy Theory, the results suggest that students are more motivated when they believe their efforts will lead to improved learning outcomes (expectancy), that these outcomes are attainable (instrumentality), and that they are personally meaningful (valence). The effective integration of AI in education requires careful consideration of factors such as faculty preparedness, equitable access, and ethical implications. Institutions must ensure that AI adoption extends beyond mere technological implementation and instead supports students’ values, promotes inclusivity, and cultivates an engaging learning environment. This study highlights the importance of a strategic, theory-informed approach to AI integration to enhance student engagement and academic success in higher education.