Generative artificial intelligence (GenAI) is reshaping pedagogical practices in higher education, especially in domains such as database management where conceptual modelling and understanding are essential. This paper proposes a shift in teaching strategies to embrace GenAI as a learning tool. We present and discuss a set of diverse exercises considering GenAI existence. We aim to foster students' critical thinking, interpretation skills, and conceptual understanding of topics such as data modeling and SQL query specification in the GenAI era. These exercises have been implemented and preliminarily used in a first-year course on Information Systems at the University of Castilla-La Mancha. Initial results suggest that they could enhance engagement and foster deeper learning. By integrating GenAI into the classroom in a thoughtful and strategic way, educators could promote understanding and prepare students for AI-augmented academic and professional environments dealing with database management.
Decision-making in industrial environments increasingly depends on the ability to align operational data with strategic goals. Data-driven approaches often focus on the available data while overlooking the specific informational needs of decision makers. To bridge this gap, this paper proposes a multi-driven approach that integrates both data-driven and requirements-driven perspectives, supported by the use of Large Language Models (LLMs). Through prompt-based interactions, LLMs generate conceptual and analytical models from metadata and user requirements, accelerating the modeling process while ensuring relevance and coherence through the integration of both data and analytical requirements. The approach is applied to a real-world industrial case characterized by operational complexity, high variability, and technological heterogeneity. The results show how the combined use of LLMs and a structured modeling approach can support the development of analytical systems that are technically feasible and strategically aligned.
Goal-Oriented Requirements Engineering (GORE) supports the development team in identifying the requirements that the system must fulfil. Commonly applied during the initial stages of requirements gathering, it focuses on identifying the system's goals, presenting their decomposition as a means of offering alternatives to satisfy them. Knowledge Acquisition in Automated Specification (KAOS) is a GORE approach that comprises a method, a software environment, and a modeling language. Modelling languages can be adaptable to various domains/application areas where the software will be developed. This way, extensions are proposed to adapt the modelling to the desired scenarios. These adaptations are referred to as extensions. KAOS has been extended to various areas, including security, adaptive systems, and aspects, among others. The creation of new KAOS extensions has been growing. It is expected to continue in the coming years, as it is necessary to adapt languages to the various existing contexts and those that emerge with the constant evolution in software development. Creating an extension is a complex task with inherent challenges, such as maintaining consistency between the developed and existing extensions. Given these facts, we recognise the need to support the creation of KAOS language extensions. This study aims to support the systematic creation of new KAOS extensions through a systematic process. The proposed process was used to create a new KAOS extension to represent accessibility concepts, which proved valid for this purpose. Finally, the PRAOS process was evaluated by KAOS extension specialists through a qualitative study.
The tutorial will introduce a new concept discovery power that can be used to characterize an enterprise modeling language. The concept is different from, but connected to, the concept of expressive power. The concept is defined as "the degree of help provided by the structure of an enterprise modeling language to expand a partly built model or fill gaps in it". The concept is realized by discovery rules that are different for different modeling languages.
While models and digital twins are heavily related, how we use these models, how we understand those models, and even what these models represent for digital twins might change over time. Existing research provides model classifications from different angles, e.g., kind of usage, model content, nature of abstraction; however, a common understanding of which kinds of models could be useful and are used in digital twin engineering and during its lifetime is missing. In this work, we present an initial faceted classification to label models based on different properties relevant for digital twins in manufacturing. We show the application of the classification on different models of a digital twin in manufacturing. The classification can make finding and reusing models in and for digital twins easier, help to identify information gaps, and give inspiration about how models might change their purpose and nature over time.
