A Systematic Literature Review of AI-Driven Intelligent Tutoring Systems in Engineering Education: Emphasizing Personalization, Feedback, and Student Monitoring

Artificial Intelligence (AI) technologies are reshaping educational environments, particularly through Intelligent Tutoring Systems (ITS) that enable personalized instruction and real-time adaptability. This Systematic Literature Review (SLR) explores the application of AI-powered ITS in engineering education, a field where learners often struggle with abstract and technically complex content. Guided by the PRISMA methodology, the review analyzes 46 peer-reviewed studies featuring components such as Natural Language Processing (NLP), adaptive learning pathways, real-time feedback, and learner progress tracking. Unlike prior reviews that emphasize general pedagogical frameworks, this work offers three key contributions: a focused synthesis of AI-based ITS solutions tailored to engineering education; a novel analysis of underexplored feature combinations, such as the integration of teacher-facing analytics with student-facing personalization; and the identification of research gaps in dual-user support for both learners and instructors. These findings provide actionable insights for researchers and developers aiming to design next-generation ITS platforms that are pedagogically robust, technically scalable, and aligned with the discipline-specific demands of engineering education.