AI Engineering

2025

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

   Johannes Schleiss, Anke Manukjan, Michelle Ines Bieber, Sebastian Lang, and Sebastian Stober

   arXiv, 2025

   Abs DOI Bib

   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.

   @article{Schleiss2026,
     author = {Schleiss, Johannes and Manukjan, Anke and Bieber, Michelle Ines and Lang, Sebastian and Stober, Sebastian},
     journal = {arXiv},
     title = {Designing an Interdisciplinary Artificial Intelligence Curriculum for Engineering: Evaluation and Insights from Experts},
     year = {2025},
     copyright = {Creative Commons Attribution Share Alike 4.0 International},
     doi = {10.48550/ARXIV.2508.14921},
     publisher = {arXiv}
   }

2024

1. Integrating AI Education in Disciplinary Engineering Fields: Towards a System and Change Perspective

   Johannes Schleiss, Aditya Johri, and Sebastian Stober

   In European Society for Engineering Education (SEFI) 2024 Annual Conference, 2024

   Accepted and presented at 52nd Annual Conference of the European Society for Engineering Education (SEFI)

   Abs DOI Bib

   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.

   @inproceedings{Schleiss2025,
     author = {Schleiss, Johannes and Johri, Aditya and Stober, Sebastian},
     booktitle = {European Society for Engineering Education (SEFI) 2024 Annual Conference},
     title = {Integrating AI Education in Disciplinary Engineering Fields: Towards a System and Change Perspective},
     year = {2024},
     note = {Accepted and presented at 52nd Annual Conference of the European Society for Engineering Education (SEFI)},
     publisher = {arXiv},
     copyright = {arXiv.org perpetual, non-exclusive license},
     doi = {10.48550/ARXIV.2410.12795},
   }

2. Towards Responsible AI - Competencies for Engineers: An Explorative Literature Review On Existing Frameworks

   Marie Decker, Johannes Schleiss, Ben Schultz, Sarah Gail Moreno, Sebastian Stober, and Carmen Leicht-Scholten

   In European Society for Engineering Education (SEFI) 2024 Annual Conference, 2024

   Abs DOI Bib

   The rapid evolution of artificial intelligence (AI) underscores the critical necessity for engineers to comprehend their responsibilities in AI use and development. This imperative requires equipping engineers with the requisite skills and knowledge to address societal challenges while ensuring that AI technologies are harnessed for societal benefit and mitigating potential risks. To support educators and course developers in identifying relevant competencies, the Responsible AI Competencies for Engineers (RAICE) framework is currently being developed collaboratively among educators, engineers, mathematicians, sociologists, and ethicists. This practice paper reports a work-in-progress based on our first explorations. In particular, we focus on Responsible AI (RAI), connected competencies, and their importance within the engineering domain. This paper answers how competencies of RAI are addressed in current frameworks for AI and ethics in general and for engineers. We report preliminary results from existing Responsible AI and engineering ethics frameworks and outline our research approach toward defining a competence framework that can guide engineering educators in developing novel learning experiences on Responsible AI and integrate these into their programs and courses.

   @inproceedings{Decker2024,
     author = {Decker, Marie and Schleiss, Johannes and Schultz, Ben and Moreno, Sarah Gail and Stober, Sebastian and Leicht-Scholten, Carmen},
     booktitle = {European Society for Engineering Education (SEFI) 2024 Annual Conference},
     title = {Towards Responsible AI - Competencies for Engineers: An Explorative Literature Review On Existing Frameworks},
     year = {2024},
     publisher = {Zenodo},
     copyright = {Creative Commons Attribution Non Commercial 4.0 International},
     doi = {10.5281/ZENODO.14256815}
   }

2023

1. AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

   Johannes Schleiss, Matthias Carl Laupichler, Tobias Raupach, and Sebastian Stober

   Education Sciences, Sep 2023

   Abs DOI Bib

   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.

   @article{Schleiss2024,
     author = {Schleiss, Johannes and Laupichler, Matthias Carl and Raupach, Tobias and Stober, Sebastian},
     journal = {Education Sciences},
     title = {AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses},
     year = {2023},
     issn = {2227-7102},
     month = sep,
     number = {9},
     pages = {954},
     volume = {13},
     doi = {10.3390/educsci13090954},
     publisher = {MDPI AG}
   }

2. Trustworthy Academic Risk Prediction with Explainable Boosting Machines

   Vegenshanti Dsilva, Johannes Schleiss, and Sebastian Stober

   In Artificial Intelligence in Education, 2023

   Abs DOI Bib

   The use of predictive models in education promises individual support and personalization for students. To develop trustworthy models, we need to understand what factors and causes contribute to a prediction. Thus, it is necessary to develop models that are not only accurate but also explainable. Moreover, we need to conduct holistic model evaluations that also quantify explainability or other metrics next to established performance metrics. This paper explores the use of Explainable Boosting Machines (EBMs) for the task of academic risk prediction. EBMs are an extension of Generative Additive Models and promise a state-of-the-art performance on tabular datasets while being inherently interpretable. We demonstrate the benefits of using EBMs in the context of academic risk prediction trained on online learning behavior data and show the explainability of the model. Our study shows that EBMs are equally accurate as other state-of-the-art approaches while being competitive on relevant metrics for trustworthy academic risk prediction such as earliness, stability, fairness, and faithfulness of explanations. The results encourage the broader use of EBMs for other Artificial Intelligence in education tasks.

   @inbook{Dsilva2023,
     author = {Dsilva, Vegenshanti and Schleiss, Johannes and Stober, Sebastian},
     pages = {463--475},
     publisher = {Springer Nature Switzerland},
     title = {Trustworthy Academic Risk Prediction with Explainable Boosting Machines},
     year = {2023},
     isbn = {9783031362729},
     booktitle = {Artificial Intelligence in Education},
     doi = {10.1007/978-3-031-36272-9_38},
     issn = {1611-3349}
   }

3. Planning Interdisciplinary Artificial Intelligence Courses For Engineering Students

   Johannes Schleiss and Sebastian Stober

   In European Society for Engineering Education (SEFI) 2023 Annual Conference, 2023

   Abs DOI Bib

   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.

   @inproceedings{Schleiss2023a,
     author = {Schleiss, Johannes and Stober, Sebastian},
     booktitle = {European Society for Engineering Education (SEFI) 2023 Annual Conference},
     title = {Planning Interdisciplinary Artificial Intelligence Courses For Engineering Students},
     year = {2023},
     publisher = {European Society for Engineering Education (SEFI)},
     doi = {10.21427/V4ZV-HR52}
   }

4. Curriculum Workshops As A Method Of Interdisciplinary Curriculum Development: A Case Study For Artificial Intelligence In Engineering

   Johannes Schleiss, Anke Manukjan, Michelle Ines Bieber, Philipp Pohlenz, and Sebastian Stober

   2023

   Abs DOI Bib

   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 AI 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.

   @article{Schleiss2023b,
     author = {Schleiss, Johannes and Manukjan, Anke and Bieber, Michelle Ines and Pohlenz, Philipp and Stober, Sebastian},
     title = {Curriculum Workshops As A Method Of Interdisciplinary Curriculum Development: A Case Study For Artificial Intelligence In Engineering},
     year = {2023},
     doi = {10.21427/XTAE-AS48},
     publisher = {European Society for Engineering Education (SEFI)}
   }

2022

1. Protecting Student Data in ML Pipelines: An Overview of Privacy-Preserving ML

   Johannes Schleiss, Kolja Günther, and Sebastian Stober

   In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 2022

   Abs DOI Bib

   The rise of Artificial Intelligence in Education opens up new possibilities for analysis of student data. However, the protection of private data in these applications is a major challenge. According to data regulations, the application designer is responsible for technical and organizational measures to ensure privacy. This paper aims to guide developers of educational platforms to make informed decisions about their use of privacy-preserving ML and, therefore, protect their student data.

   @inproceedings{Schleiss2022,
     author = {Schleiss, Johannes and Günther, Kolja and Stober, Sebastian},
     booktitle = {Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium},
     publisher = {Springer International Publishing},
     title = {Protecting Student Data in~{ML} Pipelines: An Overview of~Privacy-Preserving {ML}},
     year = {2022},
     pages = {532--536},
     doi = {10.1007/978-3-031-11647-6_109}
   }

2. Projektseminar "Künstliche Intelligenz in den Neurowissenschaften" – interdisziplinäre und anwendungsnahe Lehre umsetzen

   Johannes Schleiss, Robert Brockhoff, and Sebastian Stober

   In Anwendungsorientierte Hochschullehre zu Künstlicher Intelligenz. Impulse aus dem Fellowship-Programm zur Integration von KI-Campus-Lernangeboten, 2022

   DOI Bib

   @inbook{Schleiss2022a,
     author = {Schleiss, Johannes and Brockhoff, Robert and Stober, Sebastian},
     editor = {},
     pages = {23--31},
     publisher = {KI-Campus},
     title = {Projektseminar "Künstliche Intelligenz in den Neurowissenschaften" – interdisziplinäre und anwendungsnahe Lehre umsetzen},
     year = {2022},
     address = {Berlin},
     booktitle = {Anwendungsorientierte Hochschullehre zu Künstlicher Intelligenz. Impulse aus dem Fellowship-Programm zur Integration von KI-Campus-Lernangeboten},
     date = {2022},
     doi = {10.5281/zenodo.7319832}
   }

3. An Interdisciplinary Competence Profile for AI in Engineering

   Johannes Schleiss; Michelle Ines Bieber; Anke Manukjan; Lars Kellner and Sebastian Stober.

   In Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference, 2022

   Bib

   @inproceedings{Schleiss2023,
     author = {Kellner, Johannes Schleiss; Michelle Ines Bieber; Anke Manukjan; Lars and Stober., Sebastian},
     booktitle = {Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference},
     title = {An Interdisciplinary Competence Profile for AI in Engineering},
     year = {2022}
   }

4. Teaching AI Competencies in Engineering using Projects and Open Educational Resources.

   Johannes Schleiss; Julia Hense; Andreas Kist; Jörn Schlingensiepen & Sebastian Stober

   In Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference, 2022

   Bib

   @inproceedings{Schleiss2022b,
     author = {Stober, Johannes Schleiss; Julia Hense; Andreas Kist; Jörn Schlingensiepen  Sebastian},
     booktitle = {Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference},
     title = {Teaching AI Competencies in Engineering using Projects and Open Educational Resources.},
     year = {2022}
   }