The emergence of Education 4.0, driven by the Fourth Industrial Revolution, has reconfigured pedagogical practices in higher education through the integration of advanced digital technologies. Among these, Artificial Intelligence (AI) has gained prominence as a key enabler of personalized, data-informed, and learner-centered educational environments. This study examines the pedagogical and institutional implications of AI integration in engineering education, with particular emphasis on its role in transforming instructional design, assessment practices, and learner engagement.

Adopting a design-oriented multiple case study approach, the research synthesizes contemporary literature and analyzes four systematically implemented AI-supported interventions in undergraduate engineering programmes. Data were collected through learning analytics reports, instructor observations, learner reflections, and performance trend analyses over one academic semester. The findings indicate that AI-enabled systems support differentiated learning pathways, enhance formative assessment, promote self-regulated learning, and strengthen professional competencies when embedded within coherent pedagogical frameworks.

The study further highlights critical challenges related to ethical governance, digital equity, algorithmic transparency, and faculty preparedness. By situating AI integration within the principles of Education 4.0, this research offers empirically informed insights for educators, institutional leaders, and policymakers seeking to develop sustainable and inclusive technology-enhanced learning ecosystems.