This thesis has investigated what impact generative AI may have on higher education. Using a combination of a systematic literature study and interviews with representatives from four (4) large universities in Sweden. The findings indicate that generative AI is already a disruptive technology in teaching and learning in higher education, and that students now more easily can cheat or “mislead the examiner” using generative AI, for example by presenting ChatGPT generated text as text written by the students themselves. Even though there are some negatives with generative AI, this thesis shows that the Universities are better off embracing this technology instead of trying to work against it. So, what are the positives with generative AI in education? The fact that students can now converse with someone no matter their background, the fact that students can learn by using ChatGPT (if they are taught how to use it properly), the fact that learning how to use ChatGPT might increase the student’s efficiency and therefore increase their attractiveness on the work market when graduating. All of these benefits come with a big WARNING though. That warning is that higher education must teach the students that these tools are not miracle workers. That the tools can be wrong, and it is important that students learn how to question and criticise what is generated. Higher education has a responsibility to introduce the tools tempered by the understanding that they are not a replacement for knowledge, but only a powerful aid to enhance the knowledge that the students already possess. Finally, the study has been conducted during a particularly expansive period for generative AI and the reader should realise that the findings within this thesis represent early results in a young area of research.

Humans perceive and interact with their environment through a multitude of sensory channels. Among these, hearing plays a pivotal role, enabling humans to effectively navigate their surroundings. Sound localization, a complex process, relies on the ability of the human brain to distinguish subtle differences between propagated sound waves interacted with the subject's anthropometric features. However, when utilizing headphones in virtual environments, this natural interaction between sound waves and the human subject is altered. To replicate this phenomenon, the acquisition of head-related filters (HR filters) is necessary to transform non-spatial audio into its spatial representation. Unfortunately, the recording process of HR filters is arduous and resource-intensive, resulting in spatial gaps within datasets, particularly in regions above and below the subject, which are more challenging to capture. To address these incomplete HR filters, extrapolation methods must be employed. While distance extrapolation has been previously explored, research on extrapolation techniques for HR filters remains scarce. Hence, this study introduces a novel approach utilizing a pre-trained deep learning model known as Stable Diffusion to efficiently train the model. The results of this innovative technique showcase a remarkable level of precision and fidelity in the extrapolation of head-related filters (HR filters) for both high and low elevations for virtual auditory environments. Through the utilization of the proposed approach, HR filters are successfully extended beyond their original recording boundaries, allowing for an enhanced spatial representation of sound sources situated at varying heights. The extrapolation process not only achieves high levels of accuracy but also ensures the preservation of intricate spatial details, enabling a more immersive and realistic auditory experience for users. These findings signify a significant advancement in the field of virtual acoustics and hold substantial implications for applications such as virtual reality, gaming, and audio engineering. / Människor uppfattar och interagerar med sin omgivning genom en mängd sensoriska kanaler. Bland dessa har hörseln en avgörande roll och möjliggör för människor att effektivt navigera i sin omgivning. Ljudlokalisering, en komplex process, är beroende av människans förmåga att urskilja subtila skillnader mellan interagerande ljudvågor med människans antropometriska särdrag. När dock hörlurar används i virtuella miljöer förändras denna naturliga interaktion mellan ljudvågor och människan. För att replikera detta fenomen behövs insamling av huvudrelaterade filter (HR-filter) för att omvandla icke-spatialt ljud till dess spatiala representation. Tyvärr är inspelningsprocessen för HR-filter besvärlig och resurskrävande, vilket resulterar i spatiala luckor inom datamängder, särskilt i områden över och under subjektet som är svårare att fånga. För att åtgärda dessa ofullständiga HR-filter måste extrapolationsmetoder användas. Medan avståndsextrapolation tidigare har undersökts är forskningen kring extrapolationstekniker för HR-filter knapphändig. Därför presenterar denna studie ett nytt tillvägagångssätt som utnyttjar en förtränad djupinlärningsmodell kallad Stable Diffusion för att effektivt träna modellen. Resultaten från denna innovativa teknik visar en anmärkningsvärd precision och noggrannhet vid extrapoleringen av huvudrelaterade filter (HR-filter) för både höga och låga höjdpositioner för virtuella ljudmiljöer. Genom användning av det föreslagna tillvägagångssättet kan HR-filter framgångsrikt förlängas utanför sina ursprungliga inspelningsgränser, vilket möjliggör en förbättrad spatial representation av ljudkällor som är placerade på olika höjder. Extrapolationsprocessen uppnår inte bara hög noggrannhet utan säkerställer också bevarandet av intrikata spatiala detaljer, vilket möjliggör en mer immersiv och realistisk ljudupplevelse för användarna. Dessa resultat innebär en betydande framsteg inom området virtuell akustik och har väsentliga implikationer för tillämpningar såsom virtuell verklighet, spel och ljudteknik.

The integration of generative AI in the design industry has gained attention for its potential to enhance productivity and spark creativity. However, there is a gap in how to effectively incorporate the technology into the early stages of the design process. To address this, interviews were conducted with digital product designers at a consultancy agency to understand their current approaches to early-stage design and attitudes toward using generative AI tools into it. Findings from the interviews were used to inform the development of use case scenarios of generative AI tools in early design, which was later evaluated with the participants through observational interviews using ChatGPT and Midjourney. These findings indicate that digital product designers view generative AI as a valuable tool that can streamline the design process, aid in research and exploration, support communication and documentation, and inspire creativity. However, concerns were raised regarding trustworthiness, reliability, biased training data, and limited creativity and human nuance in these models. Designers believe in using AI-generated output as a guide, support, or inspiration, rather than a substitute for their own creativity and critical thinking, which enables an approach that allows for a mindful but effective interaction with generative AI tools.

he specter of deepfakes and artificial intelligence enabled media productioncontinues to exacerbate the fear brought on by a degraded ability to discern the real from the fake, syn- thetic, or fabricated in a networked society. While these fears are well-founded especially as they pertain to issues of involuntary pornography their introduction into an already oversaturated media landscape, if anything, extended trends in mediated indeterminacy already being fostered by the universalization of social media platforms. Sites such as Facebook, Instagram, Snapchat and TikTok, made more explicit the contingency and per- formative nature of identity. As younger generations came of age through social media they learned how to navigate and present themselves through it in novel ways unique to each platform. Oftentimes, these strategies were harmful to people’s perception of themselves and their mental health. Other times, however, it gave them the ability to experiment with new forms of identity more in line with how they actually felt. Further, more experimentation through ubiquitous mediation extended what kinds of identities are possible in general as well. In turn, the discovery and extension of identity has led to the evolution of desire. Identities and desires hitherto not possible in a physical space precipitated the creation of new objects of desire that can be pursued and materially experienced regardless of their virtual nature. Deepfakes, and now generative AI, anticipate a further, exponentially more complicated relationship with identity and desire formation through the adoption of increasingly unreal presentations of each. / Media Studies & Production

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.

The purpose of this master’s thesis was to research and evaluate how ChatGPT can be used as a tool in software developers’ daily work activities. In order to do this, the thesis was conducted in two phases, the initial exploration phase and the data collection phase. In the initial exploration phase, five senior-level developers were interviewed about their day-to-day work, opinions of generative AI, and the profession of software developers as a whole. From these interviews, a theoretical foundation for software development was formed, categorizing the daily work tasks of a software developer into either coding, communication, or planning. This theoretical foundation was then used as the basis for the tasks and interviews used during the data collection phase. In the data collection phase, seven developers, ranging from students to industry veterans, were asked to complete a set of representative tasks with the help of ChatGPT and afterward participate in an interview. The tasks were based upon the theoretical foundation of software development and aimed to serve as representative tasks that software developers have to do in their day-to-day work. Based on the tasks and interviews it was found that the use of ChatGPT did in fact help make software developers more effective when it came to coding and planning-based tasks, but not without risk since it was shown that junior developers trusted and relied more on the answers given by ChatGPT. Although ChatGPT showed a positive effect, the tooling still needs improvement, since the developers had trouble with the text formatting when completing communication-based tasks, as well as them expressing a desire for the tooling to be more integrated. However, this desire was not unexpected, since all of the developers involved showed interest in working with generative AI tooling for work-related tasks in the future.

This study examines how new technologies, particularly generative artificial intelligence (AI), have become an innovative tool for communicators and how it affects their productivity and creativity. Through a combination of literature review, theoretical frameworks, and empirical research, we analyze how communicators integrate generative AI into their work process and how this affects their workflow and conditions. The study is based on the following questions: How are software-based text chat robots used by communicators in their professional role; To what extent do communicators perceive that there is an impact on creativity and productivity when co-writing between human and machine; What opportunities and challenges do communicators imagine that software-based text chat robots can contribute to? The findings indicate that generative AI has the potential to transform the communications industry by increasing efficiency and freeing up time for more strategic thinking and creativity. Communicators report increased productivity and that generative AI has the ability to help communicators manage large text bases in an agile way. At the same time, the study also identifies challenges and potential risks with the use of generative AI. Among these challenges are issues related to ethics, quality assurance and the need to maintain human control and creative input in the creation process. Communicators are aware of these challenges and emphasize the importance of balancing automation with human skills and insight. Finally, the study highlights the opportunities and challenges of the use of generative AI for communicators and identifies areas for future research and development. By understanding the potential benefits and limitations of this technology, communicators can develop strategies to maximize its positive effects and manage its challenges effectively.

The text explores the challenges that generative AI poses to EU copyright law, focusing on two main issues: the use of copyrighted materials in developing AI models and the publication of generated digital content. The inquiry assesses the applicability of existing copyright exceptions for tasks like data mining, temporary reproduction, and database rights during the development of AI models. For the publication of generated content, the focus is on determining conditions for legal recognition as a derivative work. The text argues that generative AI falls under the flexible concepts of Arts. 3 and 4 CDSMD, with potential support for AI models generating marketing or entertainment content. However, existing exceptions do not fully support the generative AI development process. Commercial deployment of generated output may not be covered by exceptions, and its classification as a lawful derivative work depends on further clarification from the EU legislator or CJEU. The text suggests that non-authorial output should be allowed as derivative works, considering the low threshold for originality and recognizability criteria. To be lawful, derivative AI works should incorporate original parts that fade into the background, with personal style not protected by copyright but considered in an adapted derivatives test. Fair remuneration is proposed for generative AI services to address economic impacts on creatives.

This study investigates the utilization of Large Language Models for Test Case Generation. The study uses the Large Language model and Embedding model provided by Llama, specifically Llama2 of size 7B, to generate test cases given a defined input. The study involves an implementation that uses customization techniques called Retrieval Augmented Generation (RAG) and Prompt Engineering. RAG is a method that in this study, stores organisation information locally, which is used to create test cases. This stored data is used as complementary data apart from the pre-trained data that the large language model has already trained on. By using this method, the implementation can gather specific organisation data and therefore have a greater understanding of the required domains. The objective of the study is to investigate how AI-driven test case generation impacts the overall software quality and development efficiency. This is evaluated by comparing the output of the AI-based system, to manually created test cases, as this is the company standard at the time of the study. The AI-driven test cases are analyzed mainly in the form of coverage and time, meaning that we compare to which degree the AI system can generate test cases compared to the manually created test case. Likewise, time is taken into consideration to understand how the development efficiency is affected. The results reveal that by using Retrieval Augmented Generationin combination with Prompt Engineering, the system is able to identify test cases to a certain degree. The results show that 66.67% of a specific project was identified using the AI, however, minor noise could appear and results might differ depending on the project’s complexity. Overall the results revealed how the system can positively impact the development efficiency and could also be argued to have a positive effect on the software quality. However, it is important to understand that the implementation as its current stage, is not sufficient enough to be used independently, but should rather be used as a tool to more efficiently create test cases.

The development of artificial intelligence (AI) has revolutionised the way we interact with technology and each other, both in society and in professional careers. Although they come with great potential for productivity and automation, AI systems have been found to exhibit biases that reflect and perpetuate existing societal inequalities. With the recent rise of artificial intelligence tools exploiting the large language model (LLM) technology, such as ChatGPT, Bing Chat and Bard AI, this research project aims to investigate the extent of AI bias in said tools and explore its ethical implications. By reviewing and analysing responses to carefully crafted prompts generated by three different AI chatbot tools, the author will intend to determine whether the content generated by these tools indeed exhibits patterns of bias related to various social identities, as well as compare the extent to which such bias is present across all three tools. This study will contribute to the growing body of literature on AI ethics and inform efforts to develop more equitable and inclusive AI systems. By exploring the ethical dimensions of AI bias in selected LLMs, this research will shed light on the broader societal implications of AI and the role of technology in shaping our future.