Date
Publisher
arXiv
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.
Study at a glance
- Question: How well does a novel interdisciplinary AI engineering curriculum align with target competencies according to educators and industry experts?
- Setting and data: Five universities in a regional state in Germany; module catalog descriptions and 6 focus groups with 19 educators and industry experts.
- Method: Mixed methods case study combining quantitative curriculum outcome mapping with qualitative focus group interviews.
Why it matters
As artificial intelligence transforms professional practice, higher education needs AI competencies integrated across disciplines. However, research on creating interdisciplinary AI curricula remains limited, requiring new forms of cross-disciplinary collaboration.
Key findings
- Curriculum mapping and focus groups confirmed the program aligns well with targeted competencies overall, with average fit rated 3.89 out of 5 across all 19 participants.
- Educators involved in designing the curriculum rated its fit significantly higher (mean 4.50) than non-participating educators (mean 3.17) and showed stronger program ownership.
- Participants valued the interdisciplinary, practical project focus, but raised concerns regarding student overload, integrating different domains, and missing topics like deployment, preprocessing, and generative AI.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
Who age?
Study design
