Integrating Artificial Intelligence Tools in Computer Education: A Study of Student Engagement and Learning Outcomes

Abstract

The integration of artificial intelligence tools is increasingly common in higher education, but little empirical research exists on their effects on student engagement and learning outcomes in Saudi universities. This study examines the relationships among AI tools adoption, student engagement, and perceived learning outcomes among undergraduate students in computer education programs. A cross-sectional design was employed to collect survey data from 523 students across two universities in Saudi Arabia. Quantitative analysis using Structural Equation Modeling (SmartPLS) indicated positive and significant relationships between AI tools adoption, perceived usefulness, perceived ease of use, and perceived learning outcomes. AI tools adoption was the strongest predictor of student behavioral engagement, whereas perceived usefulness was the strongest predictors of student cognitive engagement. All three engagement dimensions were positively associated with perceived learning outcomes, with cognitive engagement being the strongest predictor. The findings suggest that the educational value of AI tools may depend not only on access and usability but also on their capacity to support cognitively meaningful participation. This study offers empirical evidence on technology adoption in Saudi higher education, combines student engagement and learning outcomes within a single framework, and extends the current understanding of AI usage in learning in non-Western contexts.