TLDR: This study evaluates a new interdisciplinary undergraduate curriculum for AI in engineering, developed collaboratively across five universities. Using curriculum mapping and expert interviews, it finds the curriculum effectively aligns with targeted competencies, is practical, and is well-received by educators and industry. Key insights highlight the importance of stakeholder participation, the opportunities and challenges of interdisciplinary integration, and the need for strong communication among educators to ensure depth and coherence in AI education.
As Artificial Intelligence (AI) continues to reshape professional landscapes, the demand for AI-related skills in higher education is rapidly increasing. This growing need highlights a critical gap in research regarding the effective implementation of AI education within study programs, particularly those requiring collaboration across various disciplines.
A recent study, titled Designing an Interdisciplinary Artificial Intelligence Curriculum for Engineering: Evaluation and Insights from Experts, addresses this challenge by exploring the perspectives of different stakeholders involved in developing an interdisciplinary curriculum. The research specifically focuses on a novel undergraduate program in AI in engineering, offering valuable insights into how such programs can be effectively designed and evaluated.
A Collaborative Approach to Curriculum Development
The study, conducted by Johannes Schleiss, Anke Manukjan, Michelle Ines Bieber, Sebastian Lang, and Sebastian Stober from Otto von Guericke University Magdeburg, Germany, employed a mixed-methods approach. This involved quantitative curriculum mapping to assess the alignment of the curriculum with targeted competencies, alongside qualitative focus group interviews with both academic educators and industry experts.
The case study centered on a Bachelor’s program in AI Engineering, a collaborative effort across five universities. This seven-semester curriculum (210 credit points) is structured into a core foundation in semesters 1-4, covering engineering, math, computer science, and AI, followed by a choice of five specializations. These specializations allow students to focus on applying AI technology within specific engineering domains, such as agricultural economy and technology, biomechanics and smart health technologies, green engineering, manufacturing, production and logistics, and mobile systems and telematics.
Evaluating Quality and Effectiveness
The evaluation focused on several key quality criteria: relevance (the intervention’s necessity and basis in current research), consistency (logical and cohesive curriculum structure), expected practicality (anticipated ease of use), and expected effectiveness (anticipated achievement of desired outcomes). The curriculum mapping revealed a strong alignment with the targeted competence profile, particularly emphasizing AI-related competencies, engineering specializations, process and systems-oriented work with AI, and transversal skills.
However, the mapping also identified areas with partial coverage, such as certain math and fundamental machine learning topics, and limited explicit emphasis on computer science and data science fundamentals. The curriculum showed a greater focus on AI system development and prototyping, with less coverage on deploying, monitoring, and maintaining AI systems in production environments.
Expert Perspectives: Strengths, Weaknesses, and Interdisciplinarity
Focus group interviews with 19 experts – including educators involved in the curriculum development, those not involved, and industry professionals – provided deeper qualitative insights. Overall, participants rated the curriculum’s fit with the competency profile as good. They particularly praised the program’s practical and project-oriented nature, as well as its interdisciplinary focus, which is crucial for producing employable graduates capable of working across diverse domains.
Despite the positive reception, experts also pointed out potential areas for improvement. These included perceived missing content in areas like deployment and operations, data preprocessing, and generative AI, as well as concerns about the depth of programming skills and the challenges of integrating and linking different domains effectively. The risk of student overload due to the breadth of interdisciplinary content was also a recurring theme.
Interdisciplinarity itself was seen as both a significant opportunity and a challenge. Opportunities included fostering holistic views, improving cross-disciplinary communication, and enabling skill transfer. Risks, however, involved difficulties in content integration, potential student overload, and the effort and cost associated with implementation.
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The Impact of Participation and Future Directions
A notable finding was the difference in perception between educators who participated in the curriculum development and those who did not. Participating educators demonstrated a greater sense of ownership and understanding, leading to more positive perceptions and concrete suggestions for implementation. This highlights the value of involving diverse stakeholders in the development process, especially for complex interdisciplinary programs.
Industry participants, in particular, emphasized practicality and employability, expecting graduates to possess strong problem-solving skills, professional competencies in AI and data, and the ability to communicate across disciplines. They also stressed the importance of practical experience in real-world AI implementations and deployments.
The study concludes that the developed interdisciplinary AI curriculum is expected to be effective and practical, serving as a valuable reference for other institutions aiming to integrate AI education into various disciplines. Future research should include student perspectives, compare the curriculum with similar programs globally, and investigate interventions that support interdisciplinary scaffolding and communication among educators to further enhance the quality of AI education.
