Integrating Generative AI in Higher Education: Challenges, Opportunities, and Innovations in Assessment Practices within Electrical Engineering

Abstract

The rapid integration of artificial intelligence (AI) in higher education has been significantly propelled by developments in generative AI tools, which hold considerable promise for transforming teaching and assessment methodologies. This paper explores the distinctive opportunities and challenges associated with the incorporation of generative AI in Electrical Engineering (EE) education, a discipline characterized by its emphasis on precision, experiential learning, and context-specific feedback. By concentrating on EE, the study investigates how AI tools can facilitate dynamic and personalized feedback, promote active learning, and enhance innovative assessment strategies, all while addressing the specific requirements for technical skill acquisition and critical problem-solving inherent in the field. Through the implementation of AI-driven simulations, automated control systems, and data analytics, educators can establish responsive and industry-aligned learning environments that reflect current labour market needs. Nonetheless, effective integration necessitates a thoughtful alignment of AI applications with traditional assessment frameworks, including lab-based and project-based evaluations, to ensure that crucial hands-on skills and critical thinking abilities are preserved. The implications of this research extend beyond EE, offering a comprehensive framework for the adaptation of generative AI across various higher education disciplines. By reconciling AI's technological advancements with educational goals, this study enhances the understanding of AI's role in cultivating both technical proficiency and essential human-centred skills within engineering education.