This thesis explores the impact of the usage of text-generative artificial intelligence (AI) in digital marketing on user satisfaction. Recently, concerns regarding job displacement and human expertise arose due to the efficiency and improved workflow provided by AI-powered tools. This study addresses these concerns by evaluating whether ChatGPT 3.5 is able to generate website texts with minimal human supervision while maintaining user satisfaction. Our investigation employs a mixed approach of qualitative and quantitative research, utilising controlled experiments with 14 participants aged between 20 and 31 to compare AI-generated texts with human-written texts. The controlled experiment included two identically looking prototypes, one containing human-written texts and the other containing texts generated by ChatGPT 3.5. Both prototypes had three different pages: Home, Joining and About. Additionally, two types of surveys were created, a Satisfactory Survey for each prototype and a Final Survey. The Satisfactory Survey contained Likert scales from one (1) to five (5) which enabled participants to rank the texts together with open-ended questions. The Final Survey included questions about demographics and an overall prototype preference. Having tested texts on the three different pages in each prototype on satisfaction, informativeness and appeal, the biggest difference was found in the satisfaction of the individual pages. While participants preferred human-written texts on the Home and the About page, they favoured AI-generated texts on the Joining page. Findings suggest that ChatGPT 3.5 can, with minimal human supervision, produce texts of nearly equally good satisfaction from a user perspective compared to texts written by humans. The study underscores the importance of human oversight and expertise in optimising AI-generated outputs and contributes to the ongoing discourse on integrating AI into marketing practices.

With the emergence of generative AI and its implementation in the UX-field, this qualitative case study aims to explore UX-professionals perspectives on these new tools. Through interviews and research this study aims to shed some light upon the impact of generative AI in the UX-field. The focus is set on webpage UX-designers to make the study less broad and achievable in the set time. In conclusion it becomes clear that GAI has a positive impact on UX-designers in several ways. Especially when it comes to the research part of UX-design, with GAI being used as a thorough assistant for brainstorming, workshops and for analyzing a huge amount of data. This study explores the necessity for industry evolution in tandem with technological advancements. Specialized job roles are anticipated, with emphasis on collaborative efforts between AI and humans. With GAI being used in UX-design it also raises many ethical questions about how it may be used and how it should be used. Furthermore, a lot of questions arise about copyright issues, bias, false output and lack of competence. It is clear that this field is in need of further research.

This thesis explores the integration of generative AI and large language models (LLMs) into software documentation processes, assessing their impact on developer productivity. The research focuses on the development of a documentation system powered by an LLM, which automates the creation and retrieval of software documentation. The study employs a controlled experiment followed by a survey involving software developers to quantify changes in productivity through various metrics such as effectiveness, velocity, and quality of documentation generated by the system. Background: The increasing complexity of software development necessitates efficient documentation systems. Traditional methods, often manual and time-consuming, struggle to keep pace with the dynamics of software development, potentially leading to outdated and inadequate documentation. Objectives: To investigate whether a documentation system powered by an LLM can enhance developers’ productivity in software documentation tasks by assisting developers with the creation of development documentation and facilitating the retrieval of relevant information. Method: A controlled experiment followed by a survey were conducted, wherein participants were tasked with generating and using documentation through both manual and LLM-assisted methods. The effectiveness, velocity, and quality of the documentation were measured and compared. Results: The findings indicate that the LLM-powered documentation system significantly enhances developer productivity. Developers using the system were able to produce and comprehend documentation more quickly and accurately than those using the manual method. Furthermore, the quality of the documentation, assessed in terms of comprehensibility, completeness, and readability, was consistently higher when generated by the LLM system. Conclusions: The integration of LLMs into software documentation processes can significantly enhance developer productivity by automating routine tasks and improving the quality of documentation. This supports software developers in maintaining current projects and also assists in the onboarding process of new team members by providing easier access to necessary documentation. / Denna avhandling utforskar integrationen av generativ AI och stora språkmodeller (LLM) i processer för mjukvarudokumentation, och bedömer deras inverkan på utvecklares produktivitet. Forskningen fokuserar på utvecklingen av ett dokumentationssystem drivet av en LLM, som automatiserar skapandet och hämtningen av mjukvarudokumentation. Studien använder ett kontrollerat experiment följt av en enkät som involverar professionella mjukvaruutvecklare för att kvantifiera förändringar i produktivitet genom olika mått som effektivitet, hastighet och kvalitet på dokumentation genererad av systemet. Bakgrund: Den ökande komplexiteten i mjukvaruutveckling kräver effektiva dokumentationssystem. Traditionella metoder, ofta manuella och tidskrävande, har svårt att hålla jämna steg med dynamiken i mjukvaruutveckling, vilket potentiellt kan leda till föråldrad och otillräcklig dokumentation. Syfte: Att undersöka om ett dokumentationssystem drivet av en LLM kan förbättra utvecklares produktivitet i uppgifter relaterade till mjukvarudokumentation genom att assistera utvecklare med att skapa utvecklingsdokumentation och underlätta hämtningen av relevant information. Metod: Ett kontrollerat experiment följt av en enkät genomfördes, där deltagarna hade i uppgift att generera och använda dokumentation genom både manuella och LLM-assisterade metoder. Effektiviteten, hastigheten och kvaliteten på dokumentationen mättes och jämfördes. Resultat: Resultaten visar att dokumentationssystemet drivet av LLM väsentligen förbättrar utvecklarnas produktivitet. Utvecklare som använde systemet kunde producera och förstå dokumentation snabbare och mer exakt än de som använde den manuella metoden. Vidare var kvaliteten på dokumentationen, bedömd i termer av begriplighet, fullständighet och läsbarhet, konsekvent högre när den genererades av LLM-systemet. Slutsatser: Integrationen av LLM i mjukvarudokumentationsprocesser kan väsentligen förbättra utvecklarnas produktivitet genom att automatisera rutinuppgifter och förbättra kvaliteten på dokumentation. Detta stöder inte bara mjukvaruutvecklare i att underhålla pågående projekt utan hjälper också till med introduktionen av nya teammedlemmar genom att ge enklare tillgång till nödvändig dokumentation.

The rise of conversational generative AI chatbots such as ChatGPT and Gemini is revolutionizing online interactions. Previous research has identified five categories of uses and gratifications (U&G) for users engaging with these chatbots: information seeking, task efficiency, social interaction, entertainment, and personalization. Despite the wide range of use cases, most chatbots provide one-size-fits-all text-based interactions, neglecting user preferences. Recent advancements are progressively introducing interactive features that empower users to control their interactions, such as choosing a preferred conversational style. However, despite these improvements in the industry, the interactivity in gen AI chatbots remains underexplored. This thesis serves as a user-centric foundational study of user engagement with gen AI chatbots by understanding users’ context of use across the five U&G dimensions, analyzing the limitations of text-based interactions, and proposing practical suggestions for desired interactive features.

The emergence of generative AI and platforms like ChatGPT has revolutionized the industry, triggering a widespread shift towards integrating AI models into various software applications. The Swedish Defence Research Agency is exploring the development of AI assistants for military planning. This thesis investigates the graphical user interface aspects of generative AI assistants. Following initial focus groups, a prototype was developed and assessed through user testing. The findings highlight several crucial elements to consider when designing a user interface for an AI assistant in military planning: user-friendliness, the ability to summarize documents and mission orders, an interactive map accessible to the AI assistant, and an interface to gather data and responses from the AI assistant. Implementing features crucial to the specification is essential when addressing complex tasks involving sensitive and precise data. The broader context of using generative AI represents a rapidly advancing research area that warrants close examination.

The Thermo-Calc software is a key tool in the research process for many material engineers. However, integrating multiple modules in Thermo-Calc requires the user to write code in a Python-based language, which can be challenging for novice programmers. This project aims to enable the generation of such code from user prompts by using existing generative AI models. In particular, we use a retrieval-augmented generation architecture applied to LLaMA and Mistral models. We use Code LLaMA-Instruct models with 7, 13, and 34 billion parameters, and a Mistral-Instruct model with 7 billion parameters. These models are all based on LLaMA 2. We also use a LLaMA 3-Instruct model with 8 billion parameters. All these models are instruction-tuned, which suggests that they have the capability to interpret natural language and identify appropriate options for a command-line program such as Python. In our testing, the LLaMA 3-Instruct model performed best, achieving 53% on the industry benchmark HumanEval and 49% on our internal adequacy assessment at pass@1, which is the expected probability of getting a correct solution when generating a response. This indicates that the model generates approximately every other answer correct. Due to GPU memory limitations, we had to apply quantisation to process the 13 and 34 billion parameter models. Our results revealed a mismatch between model size and optimal levels of quantisation, indicating that reduced precision adversely affects the performance of these models. Our findings suggest that a properly customised large language model can greatly reduce the coding effort of novice programmers, thereby improving productivity in material research.

Denna studie undersöker hur Generativ AI påverkar affärsmodellen hos managementkonsultbolag. Genom en kvalitativ metod med semistrukturerade intervjuer samlades data in från ett managementkonsultbolag och ett kundföretag. Resultaten visar att Generativ AI kan effektivisera processer, särskilt inom texthantering och textskapande, utan att omedelbart förändra vinstmodellen. På lång sikt förväntas dock betydande förändringar i affärsmodell en om tekniken får ett större genomslag. Slutsatsen är att Generativ AI utgör främst möjligheter, men också en utmaning för branschen, där tidig implementering och anpassning är avgörande för att behålla konkurrenskraft. / This study examines how Generative AI impacts the business model of management consulting firms. Using a qualitative method with semi-structured interviews, data was collected from a management consulting firm and a client company. The results show that Generative AI can streamline processes, particularly in text handling and text creation, without immediately altering the revenue model. However, significant changes in the business model are expected in the long term if the technology gains greater traction. The conclusion is that Generative AI primarily presents opportunities but also poses a challenge for the industry, where early implementation and adaptation are crucial for maintaining competitiveness.

Introduction and Purpose – Emerging technologies such as GenAI are revolutionizing the business landscape and drastically changing the way organizations operate. As digital transformation accelerates, more and more organizations are using GenAI to streamline operations and strengthen their competitive position. Therefore, this study explores the enabling factors and challenges when implementing GenAI in the organizational settings. Furthermore, it also examines the driving factors and leveraging benefits of GenAI in digital transformation efforts.  Methodology – The study has an explorative qualitative research design with semi-structured interviews to gather data from different industries, and business areas to collect insights into the practical applications and challenges of GenAI. This approach allowed the authors to conduct an in-depth understanding of the context and complex phenomena, GenAI. Moreover, a theoretical framework was adapted and developed from the literature review that further guided the findings and analysis.  Findings and Analysis – The findings and analysis identified enabling factors for a successful implementation; Technological, Organizational and Employees, and challenges concerning; Ethics, Regulations and Skill Gaps. Hence, these factors can be both enablers and challenges, resonating with the findings that emphasize adaptability and responsiveness in digital transformation efforts. Moreover, responsible AI is still an uncertainty due to the rapid evolvement of the technology, which means that regulatory compliance does not keep up and can act as a barrier, or enabler. It is clear that GenAI is not a straightforward path, as several enabling factors need to be in place before scaling the technology into the organizational settings. However, organizations face challenges with technological infrastructure, data management, change management, and skill gaps. Lastly, the driving factors and leveraging benefits of GenAI stems from increased business value, divided into; Efficiency and Productivity Enhancements, Innovative Product and Service Development, Knowledge Management, Personal Assistant, and Data-Driven Insights.  Discussion and Conclusion – The discussion is central to this study, where the authors integrate theory and empirical findings to generate valuable contributions. Therefore, the most central elements merges and are further discussed; Technological Readiness, Organizational Dynamics, and Responsible AI, which resulted in the creation of a new framework that further guides the academic and practical discourse. Although GenAI facilitates significant value creation, efficiency and competitive advantage, organizations are often hampered by the lack of these factors in the pursuit of digital transformation. In conclusion, this study underlines the importance of understanding that there is not one single enabling factor that needs to be in place before an implementation, rather they need to coexist with each other for a successful integration, emphasizing the transformation where technological advances meet human skills. Additionally, the human interaction and monitoring is also crucial, by setting organizational policies and standards in the quest to adapt to new regulations and ethical standards.

Context: In both commercial and open-source software development, newcomers often join the development process in the advanced stages of the software development lifecycle. Newcomers frequently face barriers impeding their ability to make early contributions, often caused by a lack of understanding. For this purpose, we have developed an LLM-based tool called SPAC-B that facilitates project-specific question-answering to aid newcomers' understanding of software projects. Objective: Investigate the LLM-based tool's ability to assist newcomers in understanding software projects by measuring its accuracy and conducting an experiment. Method: In this study, a case study is conducted to investigate the accuracy of the tool, measured in relevance, completeness, and correctness. Furthermore, an experiment is performed among software developers to test the tool's ability to help newcomers formulate better plans for open-source issues. Results: SPAC-B achieved an accuracy of 4.60 in relevance, 4.30 in completeness, and 4.28 in correctness on a scale from 1 to 5. It improved the combined mean score of the plans of the 10 participants in our experiments from 1.90 to 2.70, and 8 out of 10 participants found the tool helpful. Conclusions: SPAC-B has demonstrated high accuracy and helpfulness, but further research is needed to confirm if these results can be generalized to a larger population and other contexts of use.

Human intelligence, with its many components, has been elusive. Until recently, the emphasis has been on facts and how humans perceive them. Now, it is time to embellish these facts with emotions and commentary. Emotional experiences and expressions play a critical role in human behavior and are influenced by language and cultural diversity. In this thesis, we explore the importance of emotions across multiple languages, such as Arabic, Chinese, and Spanish. In addition, we argue for the importance of collecting diverse emotional experiences including negative ones. We aim to develop AI systems that have a deeper understanding of emotional experiences. We open-source two datasets that emphasize diversity over emotions, language, and culture. ArtELingo contains affective annotations in the aforementioned languages, revealing valuable insights into how linguistic backgrounds shape emotional perception and expression. While ArtEmis 2.0 has a balanced distribution of positive and negative emotional experiences. Studying emotional experiences in AI is crucial for creating applications that genuinely understand and resonate with users. We identify and tackle challenges in popular existing affective captioning datasets, mainly unbalanced emotion distribution, and generic captions, we pro- pose a contrastive data collection method. This approach results in a dataset with a balanced distribution of emotions, significantly enhancing the quality of trained neural speakers and emotion recognition models. Consequently, our trained speakers generate emotionally accurate and relevant captions, demonstrating the advantages of using a linguistically and emotionally diverse dataset in AI systems. In addition, we explore the cultural aspects of emotional experiences and expressions, highlighting the importance of considering cultural differences in the development of AI applications. By incorporating these insights, our research lays the groundwork for future advancements in culturally diverse affective computing. This thesis establishes a foundation for future research in emotionally and culturally diverse affective computing, contributing to the development of AI applications capable of effectively understanding and engaging with humans on a deeper emotional level, regardless of their cultural background.