This project investigates how to design a scalable, human-centered AI interaction framework for internal wealth-management workflows at Van Lanschot Kempen. It responds to operational fragmentation caused by multiple independently developed AI features, inconsistent interaction logic across tools, and varying levels of trust among staff. The research phase combined an exploratory field study (11 semi-structured interviews with private bankers, relationship managers, and investment advisors) with a thematic analysis of workflow pain points and scenario mapping of core tasks such as text writing, information lookup, and advice generation.
The iterative design process produced three interlocking outcomes. Firstly, a set of design principles that emphasize human control, transparency, clarity, and actionable feedback. Secondly, an atomic component library of reusable UI elements and panel formats to ensure consistent interaction patterns across features. Thirdly, an AI Feature and Component Selection Wizard that guides designers and product owners through interaction style, input/output configuration, and container selection, producing a concrete specification to support cross-functional handoff.
A formative evaluation with three designers and two product owners in moderated sessions revealed strong willingness to adopt the framework, increased confidence in design decisions, and clear practical value for aligning product and design stakeholders. Recommendations for refinement include clearer terminology, richer visual previews, and better mapping to the Figma component library.
The thesis contributes a method for operationalizing human-centered AI principles into a domain-tuned design system for regulated environments, along with a practical tooling approach that shortens the path from concept to implementation.

The integration of renewable energy sources has fundamentally altered the operating environment of transmission system operators (TSOs). While essential for achieving a sustainable and low-carbon energy system, their volatility introduces significant uncertainty and volatility into the power grid. For operators in control rooms, this has led to more frequent congestion events, narrower safety margins, and rising information demands across multiple fragmented systems. In this context, timely and effective decision-making becomes increasingly challenging.

The first AI-based decision support tools (DSTs) have been deployed in TSO control rooms, for example, the GridOptions tool at TenneT TSO. These DSTs remain in the assistance mode of decision support by providing context and recommendations to the human operator in a one-directional fashion. However, timely and effective decision-making under uncertainty requires bi-directional human-AI communication, feedback, and co-learning. Consequently, this study investigates how AI-based DSTs can move from an assistance mode to joint AI-human decision making.

By employing novel concepts like the Supportive AI Framework and the Joint Control Framework, this study examines how human-AI teaming can evolve across different decision-making contexts and how interfaces can dynamically adapt to situational demands in both time-critical and less urgent scenarios. In particular, human cognitive needs figure prominently in how adaptable AI-powered interfaces can support operators in maintaining grid stability under uncertainty.

Drawing from observation, collaborative interaction patterns were developed to describe how human-AI teamwork can evolve across congestion operation timeframes. These patterns reveal opportunities and challenges in dynamically allocating initiative between humans and AI, maintaining situational awareness, and safeguarding human agency in safety-critical contexts.

Ultimately, this research seeks to contribute to the design of adaptive and supportive DSTs that unify data, reduce cognitive load, and facilitate reflection and learning. By addressing the dual challenge of information overload and uncertainty, the work aims to enhance the resilience of grid control strategies, stimulating effective human-AI collaboration, and enable the continued integration of renewable energy sources into the power system.

 This study explores integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into design ideation of industrial design. Conducted at TU Delft, the research involved brainwriting and video-based design (VBD) methodologies. The primary aim was to legitimize and valiate context-injected LLMs and VLMs in supporting designers' search for inspiration through development of experiment framework. The study measured workload, user experience, acceptance of technology, divergent thinking capabilites and attitudes towards AI. It also preliminary analysed the results, focusing on qualitative insights.

Data was collected through surveys and interviews, but although eye-tracking data was also gathered, it was excluded from the analysis. The study found that while AI tools support ideation by generating diverse ideas and handling repetitive tasks, they need improvement for contextually relevant and accurate information. Designers expressed cautious optimism about AI's potential, emphasizing the need for human oversight to retain creativity and ensure context-aware assistance. 

The research highlighted optimistic leaning opinions on AI integration, noting that current AI capabilities are not yet sufficient for design ideation demands. It emphasized the necessity for AI to act as a collaborative partner, preserving the designer's critical role in the creative process. 

This report describes a graduation project for the MSc program Strategic Product Design. The client in this project is the Erasmus MC in the Netherlands and commissions this project incorporation with the Critical Alarms Lab (CAL) of the TU Delft. The Erasmus MC and CAL want to improve the experience of the Intensive Care Unit (ICU) both for patients and for the stakeholders surrounding the patient. They are also interested in using Artificial Intelligence (AI) and data to benefit ICU patients. A research project is set up to find how the experience for ICU patients can be improved and how AI can play a role in the ICU. Therefore, the research questions of this project is defined as: “How should the intensive care unit of the Erasmus MC innovate in the coming years to give critically ill patients a humane experience?”. The research resulted in a future vision that describes different values for the ICU of the Erasmus MC. Also, it is described what consider concerning humanization and Artificial Intelligence in the ICU of the future. Secondly, the research results in design guidelines that can be used by the Erasmus MC or designers to design for the ICU and ICU patient. The investigation into the ICU, the ICU patient, stakeholders with influence on the ICU and the future vision come together in a website. This website can be used to retrieve information, used as a guide or function as an inspiration for designers for healthcare.