In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and jl{events surrounding} certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the task. We propose a four-step human-AI collaboration workflow to support the jl{discovery} and clustering of these backdrops. Focusing on the painted backdrops of the American Civil War (1861 -- 1865), we present Backdrop Explorer, a content-based image retrieval (CBIR) system incorporating computer vision and novel user interactions. We evaluated Backdrop Explorer on nine users of diverse experience levels and found that all were able to effectively utilize Backdrop Explorer to find photos with similar backdrops. We also document current practices and pain points in Civil War backdrop research through user interviews. Finally, we discuss how our findings and workflow can be applied to other topics and domains. / Master of Science / In historical photo research, the presence of painted backdrops have the potential to help identify subjects, photographers, locations, and events surrounding certain photographs. Yet, research processes around these backdrops are poorly documented, with no known tools to aid in the largely manual task. We present Backdrop Explorer, a reverse image search system that helps users discover and subsequently group photos with similar backdrops. We evaluated the system and found that it effectively supported the tasks. We also document current practices and pain points in Civil War backdrop research. Finally, we discuss how our findings and system can be applied to other domains.

This study investigates the complex relationship between anthropomorphism in AI and its impact on human-AI interactions, particularly within the new concept of parasocial relationships. Through a user study analyzing interactions with the Replika chatbot, we identified key anthropomorphic design components that influence emotional bonding and user engagement. Findings indicate that human-like characteristics can enhance user satisfaction and involvement, yet excessive anthropomorphism may lead to frustration and disappointment, similar yet different from the uncanny valley concept. The paper suggests that anthropomorphism in AI design should be properly balanced so that the user has a positive experience and trust in the technology. The following paper, then, is based on thematic analysis related to user comments and brings out the fine-grained dynamics of human-AI relationships, suggesting the way forward for the future development of AI whereby emotional intelligence is integrated into the development of AI alongside the societal considerations.

Artificial intelligence is a technology that many individuals view favourably intoday's world. Where the technology contributes to a variety of benefits when usedthat can help individuals perform specific tasks. It has been shown from previousresearch that individuals' willingness to use artificial intelligence is affected by thetrust they have in the technology. It discusses how working methods and processesare affected by artificial intelligence and how it is of utmost importance to promotethe individual's trust in the technology to later promote the way artificialintelligence is used. In the previous research, various methods and models havebeen identified and demonstrated to measure and build appropriate trust in AI aswell as for various variables that affect the individual's trust and thus willingnessto use artificial intelligence as a technology.The study aims to study trust in and use of artificial intelligence from a user'sperspective where the individual is in focus. The study thus aims to study theimpact of trust on the use of the technology, any variables that affect users' trustin the technology and what is important in the implementation and use of artificialintelligence to promote trust and the use of artificial intelligence. This is tocontribute with nuanced knowledge of what is required to promote effectivecollaboration between humans and artificial intelligence when performing varioustasks.The study contains a qualitative and inductive approach where empirical data wascollected through open individual interviews and observations where respondentsinteracted with an AI tool and where they performed simple use cases. Throughcontent analysis, the empirical material has created a nuanced andknowledgeexpanding view of the phenomenon and identified aspects andvariables with an impact on individuals' trust and use of artificial intelligence.These are areas that, according to respondents, have a major impact on decisionsto use or not use artificial intelligence tools.The results of the study show that there are aspects and variables around trust thathave a major impact on decisions to use AI tools. These are thus important to keepin mind when implementing AI tools to promote trust and thus the use of artificialintelligence

The ubiquitous yet opaque logic of machine learning complicates both the design process and end-use. Because of this, much of Interaction Design and HCI now focus on making this logic transparent through human-like explanations and tight control while disregarding other, non-normative human-AI interactions as technical failures. In this thesis I re-frame such interactions as generative for both material exploration and user experience in non-purpose-driven applications. By expanding on the notion of machine learning uncertainty with play, queering, and more-than human design, I try to understand them in a designerly way. This re-framing is followed by a material-centred Research through Design process that concludes with Object Detection Radio: a ludic device that sonifies Tensorflow.js Object Detection API’s prediction probabilities. The design process suggests ways of making machine learning uncertainty explicit in human-AI interaction. In addition, I propose play as an alternative way of relating to and understanding the agency of machine learning technology.

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Interactive machine learning (IML) is a promising approach for personalising physiotherapy training. This thesis uses a research- through-design and reflective approach to explore how IML can be ecologically integrated within in-clinic physiotherapy. Domain expert interviews and observations with physiotherapists were conducted to gain a broader understanding of the physiotherapy context, the role of feedback provided to patients, and how technology could be integrated into this context. Three design artefacts were proposed to participants to provoke a discussion on the implications and current practices. Due to time constraints, the findings suggest incorporation within the consultation may be difficult. The clinic’s gym or administration time revealed to be promising alternatives. Furthermore, results highlight the importance of IML supporting richer interaction forms and the implicitness and flexibility needed to describe movement and feedback, which define physiotherapy’s hands-on approach. Togetherness was a reoccurring theme, suggesting that the input and guidance of the IML system could be something done with the patient. Finally, a reflection on the results and the study opens up a discussion of the fitness of IML in physiotherapy.

The primary objective is to explore the scalability of two-dimensional game maps while preserving certain aesthetical qualities in scaled representations. By either upscaling or downscaling the maps, features of the map inducing these aesthetical qualities may diminish. For instance, there could be alterations in the layout of corridors, rooms, characters, and treasures, as well as variations in the quantity of them. To address this, AI technology has been used as a means of preserving the feature soriginally introduced by the designer to create an alternatively scaled representation. The explorationis made possible by utilizing a game designer tool, Evolutionary Dungeon Designer (EDD), to designmaps - scale them - and generate AI-based solutions through an evolutionary algorithm. Furthermore, evaluations through both a user study and a controlled experiment were performed to analyze thescalability of game maps and the AI-generated representations. The user study showed some divisive results regarding whether the scaled or the AI-generated maps were superior. Often the AI-scaled maps were regarded as dissimilar compared to the original map. However, the AI could to some extent provide the same prevalence of some of the wanted features, but in a different design. This was also evident in the controlled experiment, where the AI managed to contain a specific feature to the same degree, but lacked the capability of making the maps similar. / Det primära målet är att utforska skalbarheten hos tvådimensionella spelkartor samtidigt som vissa estetiska egenskaper bevaras i skalförändrade representationer. Genom antingen att förstora eller förminska kartorna kan vissa egenskaper som bidrar till dessa estetiska egenskaper minska. Till exempel kan det finnas förändringar i layouten av korridorer, rum, karaktärer och skatter, liksom variationer i deras antal. För att lösa detta har AI-teknologi använts för att försöka bevara de egenskaper som ursprungligen infördes av designer, genom att skapa alternativt skalförändrade representationer. Utforskningen möjliggörs genom att använda ett speldesignverktyg, Evolutionary Dungeon Designer (EDD), för att designa kartor - skalförändra dem - och generera AI-baserade lösningar genom en evolutionär algoritm. För att analysera skalbarheten hos spelkartor och de AI-genererade representationerna genomfördes utvärderingar genom både en användarstudie och ett kontrollerat experiment. Användarstudien visade delade resultat gällande om skalförändrade kartorna eller AI-skalförändrade kartorna var mest representativ. Ofta ansågs AI-skalförändrade kartor vara olika den ursprungliga kartan. I de AI-skalade kartorna fanns det dock i viss utsträckning en liknande förekomst av de valda egenskaperna, men i en annan design. Detta var också tydligt i det kontrollerade experimentet, där AI skapade kartor som hade samma grad av en specifik egenskap, men saknade förmågan att göra kartorna lika ursprungskartan.

This paper describes a modification to the mixed-initiative co-creative tool, the Evolutionary Dungeon Designer, and a study performed to evaluate and analyze the co-creative relationship between human designer and AI. The relationship between AI and human designer in creative processes is delicate, as adjusting the autonomy of the AI can negatively affect the user experience. To perfect this type of Human-AI collaboration, further research is needed. In this thesis, different degrees of initiative of the AI are explored, to gain further understanding of mixed-initiative co-creative tools. A user study was performed on the Evolutionary Dungeon Designer, with three varying degrees of AI-initiative. The study highlighted elements of frustration that the human designer experiences when using the tool, and the behaviour in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions, as well as suggested further research.

This thesis explores the application of prompt engineering combined with human-AI interaction (HAII) to make text-to-image (TTI) models more accessible and intuitive for non-expert users. The thesis research focuses on developing an application with an intuitive interface that enables users to generate images without extensive knowledge of prompt engineering. A pre-post study was conducted to evaluate the application, demonstrating significant improvements in user satisfaction and ease of use. The findings suggest that such tailored interfaces can make AI technologies more accessible, empowering users to engage creatively with minimal technical barriers. This study contributes to the fields of Media technology and AI by showcasing how simplifying prompt engineering can enhance the accessibility of generative AI tools. / Detta examensarbete utforskar tillämpningen av prompt engineering i kombination med human-AI interaction för att göra text-till-bild modeller mer tillgängliga och intuitiva för icke-experter. Forskningen för examensarbetet fokuseras på att utveckla en applikation med ett intuitivt gränssnitt som gör det möjligt för användare att generera bilder utan omfattande kunskaper om prompt engineering. En före-efter-studie genomfördes för att utvärdera applikationen, vilket visade på en tydlig ökning i användarnöjdhet och användarvänlighet. Utfallet från studien tyder på att skräddarsydda gränssnitt kan göra AI-tekniken mer tillgänglig, och göra det möjligt för användare att nyttja det kreativa skapandet med minimerade tekniska hinder. Den här studien bidrar till områdena avmedieteknik och AI genom att demonstrera hur prompt engineering kan förenklas vilket kan förbättra tillgängligheten av AI-verktyg.

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