Exploring the Integration of Machine Learning in ICT Education to Enhance Engineering Employability Skills (Trinity Journal of Management, IT & Media (TJMITM), 2025)
This study explores the integration of machine learning (ML) into Information and Communication Technology (ICT) education to enhance engineering employability skills. This study addresses the gap between theoretical knowledge and practical applications in engineering education, which often leaves graduates underprepared for industry demands. A curriculum framework is proposed that combines foundational ML concepts with real-world projects and soft-skill development. The research employed a mixed-method approach, including a literature review, industry consultation, and pilot testing in two engineering colleges. The results showed significant improvements in students’ ability to apply ML concepts and demonstrate industry-ready competencies. The framework offers advantages such as enhanced employability, bridging academic-industry gaps, and improved academic outcomes. Future work should include scaling the curriculum across institutions and incorporating advanced ML topics. This approach aims to produce engineering graduates capable of meeting the challenges of data-driven industries.
Keywords: Machine learning (ML); Engineering; Information and Communication Technolo gy (ICT) education; Predictive analytics; Curriculum integration; Project-based learning; Practical applications; Employability skills; Industry requirements; Data-driv en decision-making