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Multidisciplinary and Interdisciplinary Teaching in the Utrecht AI Program: Why and How?
Abstract
Multidisciplinary and interdisciplinary education can provide relevant insights to ubiquitous computing and other fields. In this article, we share our experience with multidisciplinary and interdisciplinary teaching in the two-year Artificial Intelligence Research Master’s program at Utrecht University, the Netherlands. In particular, we zoom in on our motivation for, and experience with, revising courses in which non-engineering topics can be related to a more engineering inclined audience, and vice-versa.
... The initiative is built on pillars of (1) investment towards compute and adding AI-focused faculty, (2) developing AI pedagogy and including measures towards it in the quality enhancement plan, and (3) developing an understanding of AI literacy in the university and targeting curriculum development and development of new academic programs, pathways, research experiences and career development. The complexity and multidisciplinarity of developing AI programs are also highlighted by the experiences of Utrecht University's AI master program (Janssen et al. 2020) and the design of AI education offers for K-12 target groups (Chiu and Chai 2020). ...
... As introduced earlier, when looking at changes in A few curricula and course development efforts exist in the literature. Work in the context of curricula, development has been focused on the idea of multi-and interdisciplinary design highlighting the diverse nature of AI (Janssen et al. 2020;Southworth et al. 2023). The research on understanding relevant AI competencies is still in development (Tenório and Romeike 2024;Almatrafi, Johri, and Lee 2024;Wolters, Arz Von Straussenburg, and Riehle 2024). ...
Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.
... In the case of AI education, interdisciplinary teaching approaches are discussed from multiple perspectives. Janssen et al. [33] reported on experiences of an interdisciplinary AI master program. They built in six core characteristics in their course work: (1) courses are taught by multidisciplinary and interdisciplinary staff; (2) engineering techniques and theory are used hand-in-hand, connecting implementation to theoretical concepts; ...
... Moreover, research in interdisciplinary education indicates that using assessment through reflection can help students to bridge the disciplinary silos [9]. Similar to other fields, using different assessment components can be a beneficial and fair approach to account for the different experiences of students from different disciplines [33]. ...
The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the "AI Course Design Planning Framework" as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes.
... This enables a concrete insight into a central challenge of modern knowledge societies, which is characterized by highly emergent systems, organizations and situations [3]. In addition, the students also deepen competences in their own discipline by being challenged to communicate their subject-related concepts, working practices and methods in an intersubjectivity comprehensible way [4][5][6] to other people who do not have an affinity with the subject [7,8]. ...
A basic understanding of the mechanisms and implications of the use of Artificial intelligence (AI) is crucial to effectively implement AI in business and society. To achieve this goal, the research project described here aims to provide students from different disciplines with practical AI knowledge. Instead of focusing on traditional teaching approaches, students work together on transdisciplinary and interdisciplinary projects. The basis here is provided by department-specific application scenarios. Supported by learning nuggets, expert lectures and an accessible software infrastructure, students thus gain easy access to AI topics. The goal in the Faculty of Textile and Clothing Technology is to train specialists in the use of AI in the textile process chain. Students will learn how to classify image data of fibers based on a practical example. Among other things, they will generate a data set for a neural network and work closely with computer science students to implement it. The focus is on enabling students to apply AI independently and thus create the basis for new innovative ideas in industry.
... Design, while critical, can't achieve full autonomy in AI management without cross-disciplinary insights. Studies such as Woolley et al. (2010) and Hong & Page (2004) highlight the value of interdisciplinary collaboration in AI system design (Janssen et al., 2020). In addition, deep learning AI integration may adversely affect competent teams and favour lower performing ones (Zhang et al., 2021), prompting reflection on the balance of human and machine capabilities within work teams and processes. ...
The growing planetary impact of Artificial Intelligence (AI) systems reveals new social and environmental dilemmas, highlighting the entanglement of humans and non-humans. Viewing them as socio-technical systems reveals their impact as more than just products, pervasively distributed across space and time. To manage them sustainably, we need to look beyond the product-service to the temporalities, agencies, and scales of assemblages mobilised by AI. This issue explores perspectives such as data justice, posthumanism, and decolonization to critically rethink design in relation to AI across disciplinary boundaries, explaining the need for alternative perspectives on the challenges posed by technologies. Together, they emphasise the philosophical decentering of AI as an essential strategy that design should embrace to responsibly shape innovation. The article proposes a new decentralised approach for design to strategically position itself in the global public debate on AI systems.
... This interdisciplinary approach to AI education aims to resemble real-world problemsolving and motivate students through relevance (Lindvig and Ulriksen 2019;Janssen et al. 2020). At the same time, domain-specific AI education requires a good understanding of the background, and prior experiences of students (Ng et al. 2022). ...
As Artificial Intelligence (AI) becomes increasingly important in engineering, instructors need to incorporate AI concepts into their subject-specific courses. However, many teachers may lack the expertise to do so effectively or don't know where to start. To address this challenge, we have developed the AI Course Design Planning Framework to help instructors structure their teaching of domain-specific AI skills. This workshop aimed to equip participants with an understanding of the framework and its application to their courses. The workshop was designed for instructors in engineering education who are interested in interdisciplinary teaching and teaching about AI in the context of their domain. Throughout the workshop, participants worked hands-on in groups with the framework, applied it to their intended courses and reflected on the use. The workshop revealed challenges in defining domain-specific AI use cases and assessing learners' skills and instructors' competencies. At the same time, participants found the framework effective in early course development. Overall, the results of the workshop highlight the need for AI integration in engineering education and equipping educators with effective tools and training. It is clear that further efforts are needed to fully embrace AI in engineering education.
... AI education itself is often discussed in an interdisciplinary setting due to its roots in the fields of philosophy, neuroscience, psychology, cognitive science, and math (Mishra and Siy 2020). Janssen et al. (2020) reported, for example, on experiences of an interdisciplinary AI master program. Their curriculum is built around six core characteristics: (1) courses are taught by multidisciplinary and interdisciplinary staff, (2) engineering techniques and theory are used hand-in-hand, connecting implementation to theoretical concepts, (3) students are given choices in assessment and presentations to allow for individual interests, (4) highlighting relevance to practice and industry, (5) highlight multidisciplinary origins of machine learning, and (6) balancing skill levels. ...
The integration of tools and methods of Artificial Intelligence (AI) into the engineering domain has become increasingly important, and with it comes a shift in required competencies. As a result, engineering education should now incorporate competencies into its courses and curricula. While interdisciplinary education at a subject level has already been explored, the development of interdisciplinary curricula often presents a challenge. This paper investigates the use of the curriculum workshop method for developing interdisciplinary, competence-oriented curricula. Using a case study of a newly developed interdisciplinary Bachelor program for AI in Engineering, the study evaluates the instrument of the curriculum workshop. The communicative methods of the tool and various aspects of its implementation through self-evaluation procedures and surveys of workshop participants are discussed. The results show that the structure and competence orientation of the method facilitate alignment among participants from different disciplinary backgrounds. However, it is also important to consolidate the mutually developed broad ideas for the curriculum design into concrete outcomes, such as a competence profile. Interdisciplinary curriculum development needs to take into account different perspectives and demands towards the curriculum which increases complexity and requires a more structured design process. The findings of the paper highlight the importance of interdisciplinary curriculum design in engineering education and provide practical insights in the application of tools for the creation of competence-oriented curricula in curriculum workshops, thereby contributing to the development of future engineers.
We review the history of human-automation interaction research, assess its current status and identify future directions. We start by reviewing articles that were published on this topic in the International Journal of Human-Computer Studies during the last 50 years. We find that over the years, automated systems have been used more frequently (1) in time-sensitive or safety-critical settings, (2) in embodied and situated systems, and (3) by non-professional users. Looking to the future, there is a need for human-automation interaction research to focus on (1) issues of function and task allocation between humans and machines, (2) issues of trust, incorrect use, and confusion, (3) the balance between focus, divided attention and attention management, (4) the need for interdisciplinary approaches to cover breadth and depth, (5) regulation and explainability, (6) ethical and social dilemmas, (7) allowing a human and humane experience, and (8) radically different human-automation interaction.
In this column, we ask three central questions related to ubiquitous computing education. First, why is specialized ubicomp training needed? Next, what should the goal of such specialized training be? And finally, how should these goals be accomplished pedagogically? We argue that these questions should be answered by a community that supports new forms of teaching, training, and learning in ubiquitous computing.
Is artificial intelligence headed for another AI Winter? Not if we take action now.
This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.
Wie slim is, stapt in de Kunstmatige Intelligentie
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