Integrating artificial intelligence competencies in engineering education

Abstract

Artificial Intelligence (AI) emerges as a transformative technology across industries. It reshapes how we work and creates new demands for AI competencies and education. This dissertation explores the integration of AI competencies in the context of engineering education, addressing gaps in their conceptualization and operationalization at the program and course level. The state of the art provides a systems perspective on the integration of AI competencies, including a novel contextualization of AI competencies and an overview of AI education, with a focus on the role of educators. To address the question of how to conceptualize domain-specific AI competencies in engineering, the dissertation develops an AI competency profile for engineering. This profile includes professional, methodological, social, and self-competencies, validated through expert interviews and a survey with an expert panel of 32 practitioners from industry and academia. Focusing on how to develop and evaluate an interdisciplinary AI curriculum, the dissertation also proposes a structured, participatory process for creating interdisciplinary programs. This development process is evaluated through a case study, including data collection on the process with 14 participants and a self-evaluation of the facilitators. In addition, the outcome of development is validated through structured focus group interviews with 19 participants from education and industry, and curriculum outcome mapping. The findings highlight the importance of stakeholder collaboration, transparency, and scaffolding to ensure curricular integration and coherence. Finally, to address how a structured framework can support educators in integrating AI competencies into courses, the dissertation develops a prototype of a course design framework for AI courses using a design-based approach. This framework provides educators with a structural guide to adapt their teaching and effectively integrate AI competencies, empowering them to effectively navigate curricular changes. In total, over 100 educators interacted with the design prototype, while 29 participated in dedicated data collection workshops. Overall, the dissertation contributes to the emerging field of AI education by focusing on domain-specific AI education in engineering. It advances the theoretical understanding of domain-specific AI competencies and interdisciplinary curriculum design, while providing practical tools to support educators. The findings provide actionable insights for policymakers, curriculum developers, educators, and researchers to design and implement interdisciplinary AI programs. In addition, the developed AI Course Design Planning Framework serves as a practical guide for educators, enabling them to align their teaching with the evolving needs of AI education.