In the realm of Artificial Intelligence, Large Language Models (LLMs) have recently emerged as a new technology, rapidly gaining prominence across various domains due to their impressive capabilities. This paper investigates key concepts, potentials and obstacles associated with integrating LLMs into the product development process. The initial focus lies on clarifying the underlying mechanisms and capabilities of LLMs to provide a clear and practical understanding. Building upon this foundation, the exploration shifts to the potential applications of LLMs in product development. An assessment matrix evaluates the capabilities of LLMs with regards to engineering challenges, highlighting how these models could potentially improve key aspects of the development process. Additionally, the obstacles associated with implementation in a product development context are addressed.

Although sustainable change is urgently needed, more environmentally friendly alternatives are often rejected when it comes to handling and selecting products. This also applies to people with a sustainable attitude. Accordingly, the question arises as to whether and under what circumstances a sustainable attitude actually leads to sustainable product-related behavior. This basic problem is addressed here using the example of light switches made of PCR plastics, with a particular focus on the relationship between ecological sustainability attitudes and purchase intention and willingness to pay. The experimental study was conducted in a single-factorial between-person study design with n=87 test subjects. A light switch made of PCR plastic attached to a pillar served as the stimulus material. In the survey, sustainability attitudes, purchase intention and willingness to pay in particular were recorded using a questionnaire. The results of the survey revealed only a weak correlation between the attitude towards sustainability and the purchase intention, but no correlation between the attitude towards sustainability and the willingness to pay. These results indicate that the target group-specific use of PCR plastic for groups with a sustainable attitude to household appliances only makes limited sense.

Effective communication between automated vehicles (AVs) and human road users (HRU) in mixed traffic is essential for ensuring safety, trust and acceptance. However, existing research on external Human-Machine Interfaces (eHMI) for AVs often overlooks design factors and their interconnections, leading to suboptimal designs. This article presents a comprehensive framework of Human-Machine Interaction in mixed traffic, integrating different relevant stakeholders, influencing factors, and relationships. By visualizing the interactions during communication and with the surrounding environment, the framework serves as a valuable tool for research and development of eHMI, maintaining a comprehensive perspective. Key challenges include determining optimal design features, such as message transmission methods and integration into the vehicle exterior design, and considering diverse human factors, such as age, culture, and cognitive abilities. By addressing these challenges, future eHMI designs can enhance user acceptance and trust in AVs, contributing to safer and more efficient mixed traffic environments. Further research will delve into the detailed examination of design factors and the interaction between interior and exterior vehicle interfaces.

The intention to reduce greenhouse gas emissions in the aviation industry is generating high pressure to innovate, which is also leading to an increasing need for improvements in the development process, especially as this is already subject to major changes due to changing products and value creation systems. In the future, the development process should be more digitalized and information better integrated in order to optimize the supply of information to developers. To this end, the following article presents a digital twin for product development, which is being developed as part of a research project on cross-lifecycle integration through digital twins in collaboration with Rolls-Royce Electrical and other research partners. The focus is on describing the conception of the digital twin, which produces a concept that is as user centric as possible based on user stories in order to specifically serve existing needs. The concept of the digital twin will be developed based on the current as-is process and the existing system landscape as well as the use cases derived from the user stories and presented here.

As we move towards a sustainable future, designing technology plays a key role. Over the last few decades, the role of design has been steadily expanding from a purely economic benefit for products to a discipline that contributes to positive change for humanity. While the recognition of this responsibility is growing, there is a knowledge gap for design practice. What is a contemporary design approach and how does it translate into practice? We briefly review three major design movements (human-centred design, participatory design, design for sustainability) and introduce the human-driven design approach, which integrates all three perspectives into the design of technology. To explore the gap between human-centred design theory and design practice, we reflect on three use cases. Finally, this paper provides insights into how human-driven design can be implemented in practice and presents four key issues that require action: Stakeholder management, faster horses, prototyping for disruptive scenarios and methods for sustainability.

Das Konzept von digitalen Kundenzwillingen und hochpersonalisierten „Living Personas“ ermöglicht den wissensdynamischen Dialogpartner für realitätsgenaue Kontext und Bedürfnisanalysen z.B. bei komplexen Marketing-Entscheidungsprozessen. KI-basierte Werkzeuge sind ein kognitiver Begleiter für Planer, Entwerfer, Innovations-, Produkt-, und Marketing-Manager. Die neuen Personalisierungsmöglichkeiten durch individuelle und sich ständig replizierenden „Living Personas und Digital Customer Twins“ schaffen eine gesteigerte Weitsicht, Ideen- und Wissensproduktivität, sowie eine objektivierte Zielgenauigkeit bei Neuentwicklungen und Marktadressierungen. Das bestehende und neu generierte Wissen über personalisierte Kundenbedürfnisse wird durch die immersive Benutzung aktiviert und dabei kontinuierlich aktualisiert. Eine Herausforderung neuer KIbasierter Personas ist die grundsätzliche Generierung planungsrelevanter und dynamischer Kundendaten – die zweite Herausforderung ist es den Entwicklungsbeteiligten einen zugriffseffizienten und benutzeroptimierten Zugang zu dieser neuen Datenqualität im Projektalltag zu ermöglichen.

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