AI Meets UX: Shaping the User Experience for Personalised Routine Journeys in Volkswagen Cars
B.G.B.C. Silkens (TU Delft - Industrial Design Engineering)
E.D. van Grondelle – Graduation committee member (TU Delft - Industrial Design Engineering)
G.W. Kortuem – Graduation committee member (TU Delft - Industrial Design Engineering)
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Abstract
This thesis documents the development of a personalised, adaptive interface with the aim to improve the user experience of routine journeys in Volkswagen cars.
Personalised software platforms like Spotify and Netflix have marked an age of personalised software and interface design. Intelligent systems support learning through such interfaces to optimise the user experience. How can such an interface optimise the user experience in the complex automotive domain?
Driving scenarios vary significantly, from daily commutes to long-distance trips with the whole family. This results in constantly changing user needs and contextual conditions. This project investigates how an interface can adapt dynamically to these variations, using AI-driven personalisation and user feedback to optimise the experience of each journey.
A structured design approach was applied using the Vision in Product Design method within the UX domain. This included an analysis of brand identity and strategy, a deconstruction of current products into interaction qualities, the construction of a future context based on emerging trends, and the strategic positioning of a future interface concept.
The design process incorporated co-design methods to explore personalised UI configurations, focusing on balancing consistency and variety in widget selection and layout. It also identified key factors influencing user preferences in interior and interface adjustments, while uncovering opportunities for adaptive and context-aware interaction.
The developed concepts were evaluated through user testing in a seating buck setup, assessing interaction and product qualities such as usability, desirability, perceived control, and brand alignment. Qualitative feedback supported comparative analysis and version ranking of the proposed interface solutions.
In addition, the thesis contributes a set of guidelines for adaptive interface design in the automotive domain. It further investigates the role of AI in shaping human–machine interaction, including journey prediction and personalisation strategies, outlining how intelligent systems can enhance everyday driving experiences while maintaining user trust and control.
Showcase Prototype: https://www.figma.com/proto/KYfblHW9fkZeWYiRXALXCH/Final-Written-Thesis?node-id=268-11448&t=CyYl9JnR8ehmqdCs-1&scaling=scale-down-width&content-scaling=fixed&page-id=268%3A11107&starting-point-node-id=268%3A12418&show-proto-sidebar=1