Transforming Engineering Education Through Artificial Intelligence: Enhancing Learning, Innovation, and Student Engagement

Abstract

Artificial Intelligence (AI) has emerged as an important component of contemporary engineering education, offering opportunities to improve learning outcomes through data-driven instructional approaches. Technologies such as machine learning, intelligent tutoring systems, natural language processing, learning analytics, and generative AI facilitate personalised learning, automated evaluation, continuous progress tracking, and practical simulation-based experiences. This study presents the Artificial Intelligence-Driven Engineering Education Framework (AIEEF), developed to examine the impact of AI-enabled educational practices on student learning, engagement, and innovation. The framework was evaluated through a systematic review of existing research and empirical analysis of educational performance indicators. The results indicate notable improvements in student achievement, engagement, and participation in creative activities following the implementation of the proposed framework. Among the predictive models examined, Gradient Boosting achieved an accuracy of 94.2%, while overall student engagement increased from 68.7% to 89.8%. The study also considers challenges associated with AI adoption, including data privacy, algorithmic fairness, and academic integrity. The findings suggest that carefully designed AI-supported learning environments can enhance engineering education and assist institutions in preparing graduates for emerging technological and industrial requirements.