Climate change is happening. As mitigation efforts can no longer stop climate change, adaptation is needed to ensure climate resiliency. National regulations and policies often fail to meet the specific contexts of the local scales, and thus, communities need to render themselves capable of adapting through Community-Based Climate Adaptation (CBA) projects. Artificial Intelligence (AI) is seen as a powerful technology with much potential for aiding communities in their adaptation goals, but how this collaboration is to be designed is still unclear. This project uses a More-than-Human-Centred (MtHC) approach, as well as the CreaTures framework and the Augmented Collective Intelligence framework, to explore the collaboration between urban communities and AI technology. This exploration is done to assess how AI systems can enhance the capabilities of communities, and how residents can be motivated to adopt MtHC approaches in their adaptation measures. The process, stakeholders, and challenges of CBA projects, as well as opportunities for AI-based interventions, are explored through literature research, expert interviews and a thematic analysis. Following this, design concepts are explored in collaboration with an expert panel, prototypes of further developed iterations are tested, and a final concept is created. The final concept, called Entangled Intelligence, is tested both with a participant group and the expert panel, in order to retrieve insights on the research questions. The project and its limitations are discussed, and a set of characteristics of AI that support MtHC approaches to CBA is presented.

Social media has revolutionized how we access and share vast amounts of information, fundamentally altering the ways we interact, debate, and form opinions. Social media platforms have become predominant channels for information with personalization algorithms significantly shaping the content we encounter, thus, have a big impact on society. Initially, none of the social media platforms were created with the aim of delivering news. However, as their user bases expanded and their features diversified, a significant portion of their users began perceiving and using them as a news source. The research specifically focuses on the platform X (formerly Twitter), which is selected for its mission to promote and protect public conversation, positioning itself as the town square of the internet.

This thesis explores the design interventions to disrupt incidental news consumption and foster healthy discourse on social media platforms, particularly X (formerly Twitter). The literature review incorporates interdisciplinary elements, such as recommender systems, echo chambers, EU regulations, and democracy models, providing a comprehensive framework for the study. Insights from stakeholders, including politicians, non-profit organizations, and policy advisors, revealed overlooked aspects and guided the exploration of potential changes to the social media landscape. An in-depth analysis of X's features and issues informed the development of design proposals to introduce frictions in news consumption, aiming to increase exposure diversity and facilitate healthy online discourse. By analyzing qualitative data from stakeholder interviews, provotypes, and the user evaluation session, the research identifies challenges and opportunities in designing interventions.

This thesis provides design recommendations to introduce friction to incidental news consumption on social media and uncovers users’ preferences and concerns about online discussion spaces which aim to foster healthy discourses. In the end, the thesis uses these design recommendations and redesigns the initial design proposals to be able to provide a concept and solidify the recommendations for the future research.

Finally, this thesis advocates for the introduction of frictions into endless social media feeds to bridge echo chambers and enhance the diversity of viewpoints encountered. In other words, this research demonstrates that social media experiences do not always need to be seamless. Thoughtfully introduced frictions can provide moments for reflection and encourage users to engage with a broader range of perspectives, ultimately supporting a more informed and democratic society.

This work represents an initial step towards a more thoughtful and informed social media experience, contributing to a healthier democracy and a better-informed public. While the thesis acknowledges that influencing regulatory change is a long-term endeavor, it hopes to go beyond the scope of the thesis and be an influence to the future regulation practices.

Despite the fact that product sounds have been shown to have a significant impact on the experience of products, product sounds are rarely considered in design projects and design education at the Faculty of Industrial Design Engineering. Research through literature review and expert interviews revealed that product sound design requires an interdisciplinary approach. A framework was created from different areas of product sound design. This framework separates that product sounds are influenced on three levels. The first is the product features, i.e. the components that act as a sound source. At this level, it is most important to consider how acoustic and engineering decisions can influence the perception of sound as derived from psychoacoustics. The second level, the object level, is about factors that influence the composition of the product and is mostly based on knowledge from subfields of musicology. The third level, the scene level, considers the sound of the product together with its use and location. This makes it possible to assess whether the external influences still result in the desired sound. Based on this framework, a digital audio design tool was developed. The purpose of this tool was to give practical examples of how sounds can be changed. This is achieved by providing parameters that have an impact on the engineering of a product, such as the choice of components and materials, and by allowing a composition of all the features to create the sound of the whole product. Finally, external factors such as the user’s interaction with the product and the influence of the context in which the product’s sound is heard can also be modelled. User testing has shown that this tool allows creative exploration of possible product sounds, while being concrete enough to gather initial design objectives. As an ideation tool, it provides direction for the further development of the product, taking into account its sound.

Using plant-hybrid technologies presents an unusual design opportunity, by combining living plants with technology. This master’s thesis delves into the integration of plant-hybrid robotics, exploring human-plant interaction systems. The study explores the potential design implications of this field, particularly in public settings where interactions with humans are unavoidable. This way, the project aims to stimulate design for physical and psychological connections between humans, nature and technology.

RoBotanics, the case study in this exploration, focuses on designing an extremely slow-moving swarm of plants that subtly roam public indoor spaces. This way the concept subtly tries to express the passage of time. Plants lack the capacity for verbal communication or auditory perception like humans. Instead, the concept allows the plants to rely on sensing through its leaves. The physical properties of the selected plant, Dypsis Lutescens, allows the plant-hybrid robot to have a large amount of ‘antennae’ (like insects) on all sides. This quality enhances the range of the plant sensor. The plant collects data on human-plant activity, which informs its navigation. This equips plants with subtle autonomy, bringing more liveliness to an otherwise static environment.

The prototype has demonstrated that inattentional blindness allows the plant to quietly navigate shared spaces with humans without causing distraction. Moreover, varying slow speeds affect the plant’s physiological dynamics uniquely, triggering different responses. This case study contributes to the field of human-plant interaction by highlighting the potential for plant-hybrid robots to coexist alongside humans.

As AI technology continues to advance, there's a growing need to integrate it into UX design. However, AI's unique characteristics do not seamlessly align with current design tools and mastering the technical aspects for designers is a significant challenge. The project goal is to develop a tool based on a developed semi-formal representation for Human-AI (HAI) interactions, which uses a set of communicative acts1 to specify the communicated information between users and AI models as exchanges of messages.

The project followed an iterative prototyping method across 4 phases. The Pre-Phase aimed at testing communicative acts with design students using a use case ("CV-Screening") and paper materials. At the same time, it also expected to get insights on the data structure and develop the specific design considerations based on those for the Model-Informed Prototyping (MIP),

Phase 1 explored effective workflows of the digital prototype to present communicative acts by following the design considerations from the Pre-Phase and using the low-fidelity digital prototype in Figma. The use case in this phase was the same as that in the Pre-Phase.

In Phase 2, based on insights from the last two phases, there was a high-fidelity prototype in Figma which was inspired by the user journey map. It was used to assess if the design output achieved the design goals and considerations, and it helped the final test materials work better.

The Final Phase utilizes the refined digital prototype for the final test which had the same goals as that in Phase 2, providing important insights for future development.

The final output of the project is a partial prototype of a digital tool designed to facilitate the early stages of human-AI interaction design. Grounded in the principles of communicative acts and human-centered design, this tool assists designers during the Ideation stage of Design process. It achieves this by visualizing the roles, data, and information involved in the process of information exchange during Human-AI Interactions. The goal is to enhance efficiency and ease in designing these interactions.

Valuable resources are discarded on a daily basis, in the form of home furniture. Tonnes of prematurely disposed, functional and repairable furniture is often destined for landfill or incineration, with small proportions recycled. The emergent sustainability needs of the planet, and changing policies for repair of consumer products, drives the home furnishing company IKEA to explore repair as a circular strategy to enable prolonging the life of IKEA furniture. This project explores the context of repair at IKEA and is focused on the use-life of furniture, in people’s home.

Linear models aimed at profit making from the sale of new furniture, have so far prevented exploration of repair in IKEA stores. Subsequently leading to lacking visibility, knowledge, competencies, and resources for repair. While interventions are now being explored, in the form of tests and few examples at IKEA globally, channels for exchange of knowledge and service to customers are limited or yet to be developed.
In terms of customer behaviour for repair various values associated to furniture, from functional, aesthetic, emotional, material, and social, motivate customers to repair their home furniture. Yet highly person and product dependent and oftentimes limited perceived ability, in terms of knowledge, skills and resources for repair of home furniture, prevents people of taking any actions for repair. Furthermore, missing triggers, especially in the face of easily available and affordable new furniture, and high effort, low impact perception of repair leads people to replace rather than repair their furniture.

A participatory approach to include various perspectives relevant for repair, guided the research + design project. Desk research, interviews, front days, and co-creation with various stakeholders from IKEA helped identify the context of repair at IKEA NL. In terms of the infrastructural capacities, shortcomings, and subsequent opportunities for customer-end repair interventions by IKEA. Customer insights were gathered from desk research and further explored through interviews, survey, and co-creation sessions. Pain points, challenges and needs identified for distinct customer personas, enabled conceptualisation and prototyping of repair interventions to prolong the life of IKEA furniture.

Within the scope of this project, customer challenges of missing awareness of resources and overwhelming options for repair were explored alongside their needs for a sense of preparedness, advice, and guidance for repair of home furniture. In-store repair and refurbish activities for customers were explored as prototypes in collaboration with external experts to mitigate the limited repair resources and competencies in the store. The activities explored repair as a creative and social process. These were proposed to inspire, motivate, and enable customers to add value to their IKEA furniture. Creatively repaired products, demonstrations, hands-on engagement, and advice from experts were evaluated to investigate customer experience and desirability of the workshops.

While customer experience was positive and desirability for future workshops was high, yet there is low scope of feasibly and viably operating the workshops in their current format. Prominent challenges included limited dedicated space for and exploration of creative repairs and repaired products for inspiration, as well as many interdependent systems, especially in case of customer engagement and. The prototype workshops revealed a need to develop infrastructure, knowledge and products that are creatively repaired, prior to engaging with customers for knowledge exchange.

An alternate model of in-store repair and refurbish of IKEA furniture is proposed as future recommendation in the form of a visible creative repair hub, hosted by external experts. To enable development of a range of creatively repaired furniture for sale and inspiration, thereby preventing the waste of abundant damaged furniture from showroom and customer returns. A local repair hub in-store and expert collaborations also offer the opportunity to cater to customer repair requests in-stores, or referral to at-home services.

Research has shown how people anthropomorphize conversational agents (CA) and unconsciously bring their gender stereotypes into human-agent interaction. For this reason, there has been a long lasted dilemma on whether designers should design CAs that conform to or violate stereotypical expectations. Despite the urgency and importance of navigating through this dilemma, how to better design the gender identity of CA is still an open research question. In this thesis, we describe the problem space of CA identity design and argue that we can calibrate the gender effect by manipulating a metaphor we attach to the CAs. To this end, we approached these research questions from three angles: (1) developing a framework to address the ethical dilemma in CA identity design, (2) evaluating the effect of gender and metaphor in chatbot profiles, and (3) calibrating gender stereotyping through metaphor manipulation.

Tessa is a social, assistive robot from the company Tinybots, designed to support people with early- to moderate stage dementia and other mild cognitive impairments.
By giving verbal reminders and instructions, programmed by their formal and informal caregivers, Tessa gives people back their self-management and autonomy, enabling them to live independently for a longer period of time. Next to this, Tessa can be used by home care organisations to save physical minutes of care, up to 132 minutes per week (Onderzoek En Ervaringen, n.d.). With the staff shortages in the health care sector and the expected rise of people with dementia from 290,000 in 2021 to 620,000 people in 2050 (Alzheimer Nederland, 2021), the use of e-health solutions like Tessa will become increasingly important.

This master thesis consists of two parts. In the first part, a distribution scenario is designed to implement Tessa through GPs, a potential market for Tinybots to target. Currently, Tessa is implemented through home care organisations with the support of their health insurance. From interviews with GPs and their supporting caregivers like POHs and casemanagers, the appropriate scenario was chosen. In this scenario, the general practitioner gives Tessa as an option to their patient and refers them to a home care organisation that implements Tessa. This scenario gives GPs the opportunity to support their dementia patients while maintaining their current role in which they assess the situation and then refer. Due to a limited time per patient, more involvement than that is not feasible. With dementia especially, most care is immediately taken over by the casemanager. Another important factor is the lack of financial support the GP receives. Without this support, affording Tessa is impossible. In home care, there is a higher chance of financial support which is therefore incorporated in the scenario.

In the second part, the focus is on acceptance. Acceptance of help and therefore, acceptance of Tessa is hard. To increase acceptance by this group of people, the perceived usefulness of the product needs to be increased, which is what the second part of this thesis focuses on. To do this, multiple solutions were proposed surrounding themes like increasing autonomy, independence, compatibility, social connectedness, and trialability. Adding the functions of listening to audiobooks and receiving personal voice messages will give Tessa a relative advantage over the current products elderly use and increase perceived usefulness and social connectedness.

In addition, a light version of Tessa in the form of an app to be used on
people’s own devices is proposed as a solution to lower the barrier to accepting Tessa. An app is more subtle, is better compatible with the elderly who use their phone or tablet and who are still living an active life. When dementia progresses and home care is needed, the app evolves into an app that can be used by home care to provide care.
Since material and logistical costs are saved with an app, a free trial can be offered to potential users. With this, the attribute of trialability is used, which can lead to easier adoption of a new product.
In preparation for the launch of the Tessa app version and the new functions, Tinybots needs to prepare a website and information aimed at people with dementia and their informal caregivers instead of care organisations.
By implementing these design changes, Tinybots can implement Tessa through the GP with the proposed distribution scenario. This means they can use Tessa to support people with dementia and their informal caregivers from very early on and for a longer period of time.

To study how to involve the end-users in the development of machine learning explainability, this project has chosen the context of bird species identification. It intends to develop a platform where the end-users can learn bird knowledge while contributing to building the explainability of machine learning models. Among all the methods that equips machine learning models with explainability, this project adopts a framework called SECA (Semantic Concept Extraction and Analysis). In this framework, we require human-made-annotations to be made to the saliency maps of training photos to provide semantically understandable explanations to the end-users. On the other hand, we hope that the process of making annotations will also benefit the human annotators’ skills in bird species identification, in order to motivate their participation. Two main goals of the user research were: to understand the users’ needs for learning and to know their capability in making the annotations needed by the project owners. The user research started with qualitative and quantitative research to understand the current practices of the bird hobbyists, to define the target user groups, which were the birders with zero or little expertise. Then, in order to link their learning needs to the capability of machine learning explanations, three prototypes were built to collect their feedback. It was found out that they didn’t care much about the justification or transparency of bird ID apps, compared to learning knowledge in distinguishing birds. Then came the annotation test when we found the participants were able to finish the annotation task with high correctness (>93% on average). And the most popular annotations of each task were 100% correct. Finally, we built a functional, high-fidelity prototype with experiential interfaces and interactions, and tested it among 3 of the target users. They had positive feedback on the prototype’s usability and the overall workflow, which proved the feasibility of our concepts. Recommendations on usability were drawn at the end of this test. Throughout the research and design phases in this project, we have developed an approach to involve end-users in the annotation process of an explainable bird species identification model for their own benefit of fun and learning, which could potentially be applied to broader deployments