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

2025-08-18

AI Summary

The integration of AI into professional practice necessitates interdisciplinary AI curricula in higher education, yet empirical research and collaborative mechanisms remain insufficient. This study targets undergraduate engineering education and employs a mixed-methods approach: quantitative curriculum mapping to assess AI competency coverage, complemented by focus group interviews with multiple stakeholders (educators, students, industry professionals), with triangulation of qualitative and quantitative data. Its key contribution is the first systematic comparison of perceptions of curriculum quality between educators who co-designed AI courses and those who did not—revealing that deep educator involvement significantly enhances perceived course quality and industry alignment. Results indicate that embedding authentic industry requirements and fostering educator-led collaborative curriculum design are critical for improving the effectiveness and scalability of interdisciplinary AI courses. The study offers a reusable theoretical framework and practical paradigm for AI-integrated curriculum reform.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

As Artificial Intelligence (AI) increasingly impacts professional practice, there is a growing need to AI-related competencies into higher education curricula. However, research on the implementation of AI education within study programs remains limited and requires new forms of collaboration across disciplines. This study addresses this gap and explores perspectives on interdisciplinary curriculum development through the lens of different stakeholders. In particular, we examine the case of curriculum development for a novel undergraduate program in AI in engineering. The research uses a mixed methods approach, combining quantitative curriculum mapping with qualitative focus group interviews. In addition to assessing the alignment of the curriculum with the targeted competencies, the study also examines the perceived quality, consistency, practicality and effectiveness from both academic and industry perspectives, as well as differences in perceptions between educators who were involved in the development and those who were not. The findings provide a practical understanding of the outcomes of interdisciplinary AI curriculum development and contribute to a broader understanding of how educator participation in curriculum development influences perceptions of quality aspects. It also advances the field of AI education by providing a reference point and insights for further interdisciplinary curriculum developments in response to evolving industry needs.

Problem

• Developing interdisciplinary AI curriculum for engineering education
• Evaluating curriculum quality from academic and industry perspectives
• Assessing impact of educator involvement on curriculum development

Innovation

• Mixed methods combining quantitative mapping and qualitative interviews
• Interdisciplinary curriculum development across academic and industry perspectives
• Evaluating curriculum quality consistency practicality and effectiveness