Key points are not available for this paper at this time.
This paper discusses how artificial intelligence and machine learning can be incorporated into the new curricula of chemical engineering education as part of the transformation facing the challenges of Industry 4.0. It describes the ways in which AI tools, adaptive learning platforms, virtual laboratories, intelligent tutoring systems, and automatic assessments can significantly improve teaching effectiveness, resulting in corresponding increases in student learning outcomes. A set of indicative case studies from engineering programs is also analyzed to assess impacts based on a broad literature review. Results demonstrate that AI/ML applications provide opportunities for personalized instruction, leading to heightened conceptual understanding and accessible feedback support. Such benefits make AI/ML more preferable than traditional pedagogy, which often lacks scalability and adaptability to diverse circumstances. It also highlighted challenges related to data privacy, faculty preparedness, infrastructure limitations, and institutional resistance. The paper finally presents a set of practical recommendations for implementation, along with emerging trends, such as generative AI and learning analytics, that are shaping the future landscape of engineering education.
Nayef Ghasem (Tue,) studied this question.