Judith Michael

Artificial Intelligence (AI) literacy is becoming an essential competency across disciplines and stakeholders. However, AI-related education poses important difficulties, as it combines many concerns such as technical skills, ethical/legal issues, critical thinking, etc. These difficulties, combined with the variety of learners background, push towards customizable learning support. Such needs could be better addressed through machine processable representations, as those enable distribution, reuse, and automation mechanisms both from Model-Driven Engineering (MDE) and Generative AI techniques. This paper proposes a framework powered by MDE methodologies to support the conceptualization of the AI literacy domain in terms of competencies and learning scenarios. The conceptualization is realized by means of corresponding metamodels in close collaboration with pedagogy domain experts. The expressiveness of the metamodels is demonstrated in a proof-of-concept developed for AI literacy teaching.

Research software developed by domain experts enables scientific research by addressing domain-specific problems tailored to the domains and constraints of their respective fields. As such software evolves and its functional and quality requirements increase, applying software engineering practices becomes essential to manage the software system. An example of such research software is OptiGob, a decision-support tool to project CO₂e emissions among other properties for Ireland’s agriculture, forestry, and other land use (AFOLU) sector. In this paper, we present the software engineering practices applied during the evolution of OptiGob to handle newly emerging requirements and a model-driven approach to establish a foundation for future extensions such as additional AFOLU systems and characteristics in the dataset and new application features. Our solution is based on MontiGem, a code generator for data-centric applications. This new version of OptiGob enables users to build and evaluate more complex AFOLU scenario configurations while managing the growing complexity of the software through model-driven engineering.

Model-based engineering (MBE) is a powerful paradigm that leverages models as essential pillars of the development process, enabling teams to clarify requirements, streamline design, specify behavior, and perform rigorous verification and validation tasks across the entire system life cycle. Digital twins (DTs) represent revolutionary software systems that mirror cyber-physical, socio-economic, or biological entities, systems, or processes. Built from robust models and data, DTs are deployed for high-impact applications such as planning, monitoring, control, and optimization of the twinned entity. The model-centric nature of DTs has naturally ignited recent exploration into harnessing MBE for the engineering and operation of DTs. However, this organic evolution has created a fragmented landscape of (partial) solutions. To confront this challenge, this article presents a rigorous and systematic literature survey on the field of model-based DT engineering (MBDTE), accompanied by a novel taxonomy for categorizing MBDTE approaches. We also introduce crisp definitions of both the field of MBDTE and the models themselves. We conclude by highlighting research gaps and outlining avenues for further exploration.

Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments. From 447 initially identified papers, 30 primary studies met the inclusion criteria. About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE. The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information. However, only few specify concrete modeling techniques on how to incorporate accessibility needs or demonstrate fully functional systems. Insufficient details on MDE methods, i.e., transformation rules or code templates, hinder the reuse, generalizability, and reproducibility. Furthermore, limited involvement of affected users and limited developer expertise in accessibility contribute to weak empirical validation. Overall, the findings indicate that current MDE research insufficiently supports vision-related accessibility. Our paper concludes with a research agenda outlining how support for vision impairments can be more effectively embedded in MDE processes.

Digital twins promise a better understanding and use of complex systems. To this end, they represent these systems at their runtime and may interact with them to control their processes. Software engineering is a wicked challenge in which stakeholders from many domains collaborate to produce software artifacts together. In the presence of skilled software engineer shortage, our vision is to leverage DTs as means for better rep- resenting, understanding, and optimizing software engineering processes to (i) enable software experts making the best use of their time and (ii) support domain experts in producing high-quality software. This paper outlines why this would be beneficial, what such a digital twin could look like, and what is missing for realizing and deploying software engineering digital twins.