Designing an Interdisciplinary Artificial Intelligence Curriculum for Engineering: Evaluation and Insights From Experts

Abstract

As artificial intelligence (AI) increasingly impacts professional practice, higher education requires new frameworks for integrating AI competencies into degree programs. At the same time, systematic approaches to designing domain-specific AI programs are underexplored in research. This study evaluates the development of a novel undergraduate AI engineering program (210 credits, seven semesters) using formative evaluation through curriculum mapping and focus group interviews with 19 experts (educators and industry representatives), examining perceived quality, consistency, practicality, and effectiveness. Three key findings emerge: First, the conceptual program that the developed interdisciplinary AI curriculum is expected to be effective, practical, and positively validated by educators and industry. Second, educators who participated in the design process show greater ownership and systemic understanding than nonparticipants, revealing how participatory approaches could shape quality perceptions in interdisciplinary contexts. Third, while stakeholders view the interdisciplinary structure as a strength for employability, they identify practical challenges that need to be considered when implementing the program. Overall, the study contributes both a validated transferable reference model for AI engineering programs and the first understanding on the impact of participatory design in interdisciplinary contexts, advancing scholarship on AI education, and providing practical guidance for institutions developing domain-specific AI programs.