I en tid då AI-språkmodeller används alltmer i vår vardag, blir det relevant att undersöka hur det påverkar samhället. Denna studie undersöker, utifrån teorier om etik och jämställdhet, hur AI-språkmodeller i sina texter ger uttryck för mångfald, icke-diskriminering och rättvisa. Studien fokuserar på att identifiera och analysera förekomsten av könsbias i AI-språkmodellernas svar samt hur det påverkar människors förtroende för dessa system. En fallstudie genomfördes på tre AI-språkmodeller - ChatGPT 3.5, Gemini och Llama-2 70B, där data insamlades via intervjuer med dessa modeller. Därefter gjordes intervjuer med mänskliga informanter som reflekterade över AI-språkmodellernas svar. AI-språkmodellerna visade en obalans i hur de behandlar kvinnor och män vilket kan förstärka befintliga könsstereotyper. Detta kan påverka människors förtroende för AI-språkmodeller och informanterna lyfte problematiken om vad neutralitet och rättvisa innebär. För att skapa mer ansvarsfulla och rättvisa AI-system krävs medvetna insatser för att integrera etiska och jämställdhetsperspektiv i AI-utveckling och användning. / In a time when Large Language Models (LLMs) are increasingly used in our daily lives, it becomes important to investigate how this affects society. This study examines how LLMs express diversity, non-discrimination, and fairness in texts, based on theories of ethics and gender equality. The study focuses on identifying and analyzing the presence of gender bias in the responses of LLMs and how this impacts people's trust in these systems. A case study was conducted on three LLMs: ChatGPT 3.5, Gemini, and Llama-2 70B, where data was collected through interviews with them. Subsequently, interviews were conducted with human informants who reflected on the LLMs’ responses. The LLMs showed imbalance towards gender, potentially reinforcing existing gender stereotypes. This can affect people's trust in LLMs, and the informants highlighted the issue of what neutrality and fairness entail. To create more responsible and fair AI systems, conscious efforts are required to integrate ethical and equality perspectives into AI development and usage.

The integration of Large Language Models (LLMs) such as GPT-4 and Gemini into healthcare, particularly for elderly care, represents a significant opportunity in the use of artificial intelligence in medical settings. This thesis investigates the capabilities of these models to understand and respond to the healthcare needs of older adults effectively. A framework was developed to evaluate their performance, consisting of specifically designed medical scenarios that simulate real-life interactions, prompting strategies to elicit responses and a comprehensive user evaluation to assess technical performance and contextual understanding.  The analysis reveals that while LLMs such as GPT-4 and Gemini exhibit high levels of technical proficiency, their contextual performance shows considerable variability, especially in personalization and handling complex, empathy-driven interactions. In simpler tasks, these models demonstrate appropriate responsiveness, but they struggle with more complex scenarios that require deep medical reasoning and personalized communication.  Despite these challenges, the research highlights the potential of LLMs to significantly enhance healthcare delivery for older adults by providing timely and relevant medical information. However, to realize a truly effective implementation, further development is necessary to improve the models’ ability to engage in meaningful dialogue and understand the nuanced needs of an aging population.  The findings underscore the necessity of actively involving older adults in the development of AI technologies, ensuring that these models are tailored to their specific needs. This includes focusing on enhancing the contextual and demographic awareness of AI systems. Future efforts should focus on enhancing these models by incorporating user feedback from the older population and applying user-centered design principles to improve accessibility and usability. Such improvements will better support the diverse needs of aging populations in healthcare settings, enhancing care delivery for both patients and doctors while maintaining the essential human touch in medical interactions. / Integrationen av stora språkmodeller (LLMs) såsom GPT-4 och Gemini inom sjukvården, särskilt inom äldrevård, representerar betydande möjligheter i användningen av artificiell intelligens i medicinska sammanhang. Denna avhandling undersöker dessa modellers förmåga att förstå och effektivt svara på äldres vårdbehov. För att utvärdera deras prestanda utvecklades ett ramverk bestående av specifikt utformade medicinska situationer som simulerar verkliga interaktioner, strategier för att framkalla relevanta svar från modellerna och en omfattande användarutvärdering för att bedöma både teknisk prestanda och kontextuell förståelse.  Analysen visar att även om LLMs såsom GPT-4 och Gemini visar på hög teknisk prestationsförmåga, är dess kontextuella förmåga mer begränsad, särskilt när det gäller personalisering och hantering av komplexa, empatidrivna interaktioner. Vid enklare uppgifter visar dessa modeller på en lämplig responsivitet, men de utmanas vid mer komplexa scenarier som kräver djup medicinsk resonemang och personlig kommunikation.  Trots dessa utmaningar belyser denna forskning potentialen hos LLMs att väsentligt förbättra vårdleveransen för äldre genom att tillhandahålla aktuell och relevant medicinsk information. Däremot krävs ytterligare utveckling för att verkligen möjliggöra en effektiv implementering, vilket inkluderar att förbättra modellernas förmåga att delta i en meningsfull dialog och förstå de nyanserade behoven hos äldre patienter.  Resultaten från denna avhandling understryker nödvändigheten av att aktivt involvera äldre individer i utvecklingen av AI-teknologier, för att säkerställa att dessa modeller är skräddarsydda för deras specifika behov. Detta inkluderar ett fokus på att förbättra den kontextuella och demografiska medvetenheten hos AI-system. Framtida insatser bör inriktas på att förbättra dessa modeller genom att integrera användarfeedback från äldre populationer och tillämpa principer för användarcentrerad design för att förbättra tillgänglighet och användbarhet. Sådana förbättringar kommer att bättre stödja de mångsidiga behoven hos äldre i vårdsammanhang, förbättra vårdleveransen för både patienter och läkare samtidigt som den väsentliga mänskliga kontakten i medicinska interaktioner bibehålls.

The integration of Artificial Intelligence (AI) in dental radiography not only presents an opportunity but also holds immense potential to enhance diagnostic accuracy and efficiency. This study addresses the exciting challenge of leveraging AI, specifically a generative pre-trained transformer model, to interpret dental panoramic X-rays, a task traditionally reliant on human expertise. The central purpose of the study is to evaluate the diagnostic capabilities of this AI model compared to professional dental evaluations, focusing on its accuracy and consistency, thereby paving the way for a promising future in dental diagnostics. The research involved a sample of 35 dental panoramic X-rays obtained from Flexident AB, anonymized and annotated by a panel of dental professionals. The study was conducted in two stages: Stage One tested the AI model in three different methods: 1- without any annotations, 2- with numbered teeth, and 3- with colored circles highlighting areas of interest. Stage Two involved training a specialized GPT model with domain-specific knowledge. Key findings indicate that the AI model, when provided with detailed visual annotations, achieved diagnostic accuracy comparable to that of dental professionals, as statistical analysis showed no significant differences between the golden standard (dentist group) and the visually annotated group (P>0.05). However, the model struggled with unannotated images, highlighting the importance of structured input. The research underscores the potential of language-based AI in medical imaging while emphasizing the need for detailed input to optimize performance. This study is pioneering in applying a generative pre-trained transformer model for dental diagnostics, opening new avenues for AI integration in healthcare. / Integrationen av artificiell intelligens (AI) inom tandradiografi innebär inte bara en möjlighet utan har också en enorm potential att förbättra diagnostisk noggrannhet och effektivitet. Denna studie tar upp den spännande utmaningen att utnyttja AI, specifikt en generativ förtränad transformer-modell, för att tolka panoramaröntgenbilder av tänder, en uppgift som traditionellt är beroende av mänsklig expertis. Studiens centrala syfte är att utvärdera de diagnostiska förmågorna hos denna AI-modell jämfört med professionella tandläkarbedömningar, med fokus på dess noggrannhet och konsekvens, vilket banar väg för en lovande framtid inom tanddiagnostik. Forskningen omfattade ett urval av 35 panoramaröntgenbilder av tänder erhållna från Flexident AB, anonymiserade och annoterade av en panel av tandläkare. Studien genomfördes i två steg: Steg ett testade AI-modellen på tre olika sätt: 1- utan några annoteringar, 2- med numrerade tänder och 3- med färgade cirklar som markerade intressanta områden. Steg två involverade träning av en specialiserad GPT-modell med domänspecifik kunskap. Nyckelresultat visar att AI-modellen, när den tillhandahölls detaljerade visuella annotationer, uppnådde en diagnostisk noggrannhet jämförbar med professionella tandläkare, då statistisk analys visade inga signifikanta skillnader mellan guldstandarden (tandläkargruppen) och den visuellt annoterade gruppen (P>0,05). Modellen hade dock svårigheter med icke-annoterade bilder, vilket understryker vikten av strukturerad inmatning. Forskningen betonar potentialen hos språkbaserad AI inom medicinsk avbildning och behovet av detaljerad inmatning för att optimera prestanda. Denna studie är banbrytande i sin tillämpning av en generativ förtränad transformer-modell för tanddiagnostik, vilket öppnar nya möjligheter för AI-integrering inom sjukvården.

The recent release of AI tools for public use allows for the development of novel teaching approaches for goals that often present challenges in the classroom, such as the need for personalized learning materials. The current study enlists a four-week ChatGPT-based personalized learning intervention in tandem with a teacher questionnaire and interviews in two upper-secondary schools in Southern Sweden to investigate English teacher perceptions of the benefits and challenges of using AI for personalized language learning. In addition, the intervention investigates the potential effectiveness of personalized learning assignments using ChatGPT on the development of students’ grammar abilities in a specific, local classroom context to both address a local need at the school in question and to serve as a proof of concept for more broad-based, future research on the use of these tools for this purpose. The questionnaire revealed that teachers initially had some concerns regarding the accuracy, reliability, and practical implementation of such tools. However, the intervention was found to significantly reduce grammar errors in student writing, and in follow-up interviews, teachers reported feeling more receptive to such approaches after interacting with the tools and seeing the beneficial results. These findings demonstrate that teachers may be hesitant to implement AI tools, which underscores the importance of training and first-hand use for promoting their successful adoption into pedagogical practices. In addition, the findings suggest that AI-based tools for personalized language learning may also be successful in a broader educational context. Finally, certain limitations, such as the small sample size, are acknowledged which emphasizes that further research is necessary to acquire a more comprehensive understanding of personalized learning using AI-based tools like ChatGPT.

The rapid growth of textual data in the digital age presents unique challenges in domain-specific text classification, particularly the scarcity of labeled data for many applications, due to expensive cost of manual labeling work. In this thesis, we explore the applicability of prompt-learning method, which is well-known for being suitable in few-shot scenarios and much less data-consuming, as an emerging alternative to traditional fine-tuning methods, for domain-specific text classification in the context of customer-agent interactions in the retail sector. Specifically, we implemented the entire prompt-learning pipeline for the classification task, and, our investigation encompasses various strategies of prompt-learning, including fixed-prompt language model tuning strategy and tuning-free prompting strategy, along with an examination of language model selection, few-shot sampling strategy, prompt template design, and verbalizer design. In this manner, we assessed the overall performance of the prompt-learning method in the classification task. Through a systematic evaluation, we demonstrate that with the fixed-prompt language model tuning strategy, based on relatively smaller language models (e.g. T5-base with around 220M parameters), prompt-learning can achieve competitive performance (close to 75% accuracy) even with limited labeled data (up to merely 15% of full data). And besides, with the tuning-free prompting strategy, based on a regular-size language model (e.g. FLAN-T5-large with around 770M parameters), the performance can be up to around 30% accuracy with detailed prompt templates and zero-shot setting (no extra training data involved). These results can offer valuable insights for researchers and practitioners working with domain-specific textual data, prompt-learning and few-shot / zero-shot learning. The findings of this thesis highlight the potential of prompt-learning as a practical solution for classification problems across diverse domains and set the stage for future research in this area.

Användningen av Artificiell Intelligens (AI) är utbredd bland verksamma företag idag, likväl privatpersoner. Det har blivit en integrerad del av vårt samhälle som ofta går obemärkt förbi. Allt från face recognition, självkörande bilar och automatisering inom arbetsrelaterade områden, har AI onekligen påverkat omvärlden. I takt med att AI-modeller fortsätter att utvecklas tillkommer även farhågor om dess påverkan på jobb, tillhörande säkerhetsrisker och etiska dilemman. Uppsatsens litteratur hjälper till att skildra AI historiskt, i nutid, men även ge en uppfattning om vart den är på väg. Den AI-modell som i nuläget har väckt störst uppmärksamhet är ChatGPT. Dess potential tycks inte ha några gränser, därmed uppstod relevansen för att öka kunskapen kring AI-modellen. Vidare gjordes en avgränsning, där fokusområdet var att undersöka hur ChatGPT kan generera kodkommentarer och potentiellt agera som ett hjälpmedel vid kommentering av källkod. I samband med avgränsningen och fokusområdet bildades även forskningsfrågan: Large Language Models: Bedömning av ChatGPT:s potential som verktyg för kommentering av kod För att besvara forskningsfrågan har avhandlingen varit baserat på en kvalitativ ansats, där urvalet av respondenter har varit programmerare. Den primära datainsamlingen har genomförts via två semistrukturerade intervjuer, varav den inledande innefattade initiala känslor kring ChatGPT och övergripande fakta om respektive intervjuobjekt. Vidare gjordes det en observation för att få en inblick i hur AI-modellen används av programmerare, för att avslutningsvis göra en uppföljande intervju post-observation i syfte att samla tankarna från intervjuobjekten efter användning av ChatGPT för att generera kodkommentarer. Baserat på den insamlade empirin kunde studien konkludera vissa begränsningar i den nuvarande modellen, inte minst behovet av tydliga instruktioner. Trots brister visar ChatGPTs framställning potential att vara en betydande resurs för kommentering av kod i framtiden. Resultaten indikerar att modellen kan generera relativt passande kommentarer i de analyserade kodkodstycken. Emellertid uttryckte deltagarna under de avslutande intervjuerna generellt sett att kommentarerna var redundanta och saknade betydande värde för att öka förståelsen av källkoden. Respondenterna diskuterade dock möjligheterna att använda ChatGPT i framtiden, men underströk behovet av förbättringar för att göra det till en tillförlitlig metod inom arbetsrelaterade situationer. / The usage of Artificial Intelligence (AI) is widespread among both companies and individuals today. It has become an integrated part of our society, often going unnoticed. From face recognition and self-driving cars to automation in work-related areas, AI has undeniably impacted the world. As AI models continue to evolve, concerns about their impact on jobs, associated security risks, and ethical dilemmas arise. The literature in this essay helps portray AI historically, in the present, and provides an insight into its future direction. The AI model that has currently garnered the most attention is ChatGPT. Its potential seems limitless, which prompted the relevance of increasing knowledge about the AI model. Furthermore, a delimitation was made, where the focus area was to investigate how ChatGPT can generate code comments and potentially act as a tool for commenting source code. As part of the research focus and scope, the research question was formulated: "Large Language Models: Assessment of ChatGPT's Potential as a Tool for Code Commenting." To answer the research question, the thesis adopted a qualitative approach, with programmers as the selected respondents. The primary data collection was conducted through two semi-structured interviews, where the initial interview involved capturing initial impressions of ChatGPT and gathering general information about the interviewees. Additionally, an observation was carried out to gain insights into how programmers utilize the AI model, followed by a post-observation interview to gather the interviewees' thoughts after using ChatGPT to generate code comments. Based on the collected empirical data, the study was able to conclude certain limitations in the current model, particularly the need for clear instructions. Despite these limitations, ChatGPT's performance demonstrates the potential to be a significant resource for code commenting in the future. The results indicate that the model can generate relatively suitable comments in the analyzed code snippets. However, during the concluding interviews, participants generally expressed that the comments were redundant and lacked significant value in enhancing the understanding of the source code. Nevertheless, the respondents 2 discussed the possibilities of using ChatGPT in the future, while emphasizing the need for improvements to establish it as a reliable method in work-related situations.

Påskyndat av teknik går utvecklingen snabbare än någonsin. Generativ AI har blivit tillgänglig för allmänheten. Det ger möjligheter för verksamheter att nyttja AI-teknik utan större insatser och kunskap. Detta skiftar förutsättningarna inom digital innovation. Denna nya aktör skapar gap i litteraturen, där tidigare forskning behöver omvärderas. Ett viktigt forskningsområde är hur användningen av generativ AI påverkar digital innovation. En annan aspekt är hur innovatörer kan nyttja, och förhålla sig till generativ AI inom innovationsprocessen. För att undersöka detta har en kvalitativ studie genomförts, där empiri har samlats in genom åtta intervjuer. Studien har resulterat i en tematisk modell med följande teman: Generativ AI som en kollega; Generativ AI som resurs för digital innovation; Generativ AI ökar tillgängligheten till AI-teknik; Känslor gällande generativ AI; Problematik gällande generativ AI; Spridd och differentierad syn på digital innovation. Studien visar att generativ AI kan påverka digital innovation genom de resulterande temana. Vidare relateras dessa teman till innovationsprocessen, där en modifierad processmodell för innovation har tagits fram. Då användningen av generativ AI är ett relativt nytt fenomen är det sannolikt att innovatörer framöver kommer att öka sin användning av verktyget, vilket medför att fynden från denna studie riskerar att snabbt bli utdaterade. Vidare forskning bör därför utföra liknande studier med jämna mellanrum, för att fånga upp nya erfarenheter som uppstår av den ökade användningen. / Accelerated by technology, development is progressing faster than ever. Generative AI has become accessible to the general public. It provides opportunities for businesses to leverage AI technology without significant efforts and expertise. This shifts the conditions within digital innovation. This new actor creates gaps in the literature, where previous research needs to be reevaluated. An important research area is how the use of generative AI affects digital innovation. Another aspect is how innovators can utilize and engage with generative AI in the innovation process. To investigate this, a qualitative study has been conducted, where empirical data has been collected through eight interviews. The study has resulted in a thematic model with the following themes: Generative AI as a colleague; Generative AI as resource for digital innovation; Generative AI increases accessibility to AI technology; Emotions regarding generative AI; Challenges regarding generative AI; Diverse and differentiated views on digital innovation. The study shows that generative AI can affect digital innovation through the resulting themes. Furthermore, these themes were related to the innovation process, where a modified process model for innovation has been developed. Since the use of generative AI is a relatively new phenomenon, it is likely that innovators will increase their use of the tool in the future. This may render the findings from this study quickly outdated. Further research should therefore conduct similar studies at regular intervals to capture new experiences arising from increased usage.

<p dir="ltr">Recent advancements in large language models such as ChatGPT and AU Large allow for the effective integration and application of LLMs into network devices such as switches and routers in terms of the ability to play a role in configuration and management. The given devices are an essential part of every network infrastructure, and the nature of physical networking topologies is complex, which leads to the need to ensure optimal network efficiency and security via meticulous and precise configurations.</p><p dir="ltr">The research explores the potential of an AI-driven interface that utilizes AU Large to streamline, enhance, and automate the configuration process of network devices while ensuring that the security of the whole process is guaranteed by running the entire system on-premise. Three core areas are of primary concern in the given study: the effectiveness of integrating the AU Large into network management systems, the impact on efficiency, accuracy, and error rates in network configurations, and the scalability and adaptability to more complex requirements and growing network environments.</p><p dir="ltr">The key performance metrics evaluated are the error rate in the generated configurations, scalability by looking at the performance as more network devices are added, and the ability to generate incredibly complex configurations accurately. The high-level results of the critical performance metrics show an evident correlation between increased device count and increased prompt complexity with a degradation in the performance of the AU Large model from Mistral AI.</p><p dir="ltr">This research has significant potential to alter preset network management practices by applying AI to make network configuration more efficient, reduce the scope for human error, and create an adaptable tool for diverse and complex networking environments. This research contributes to both AI and network management fields by highlighting a path toward the “future of network management.”</p>

This thesis investigates the use of Artificial Intelligence, specifically the Large Language Model (LLM) application ChatGPT in the context of qualitative user research, with the goal of enhancing the user research interview analysis process. Through an empirical study where ChatGPT was used in the process of a typical user research insight analysis, the limitations and opportunities of the AI tool are examined. The study's results highlight the most significant insights from the empirical investigation, serving as examples to raise awareness of the implications of using ChatGPT in the context of user interview analysis. The study concludes that ChatGPT has the potential to enhance the interpretation of primarily individual interviews by generating well-articulated summaries, provided their accuracy can be verified. Additionally, ChatGPT may be particularly useful in low-risk design projects where the consequences of potential misinterpretations are minimal. Finally, the significance of clearly articulated written instructions for ChatGPT for best results is pointed out.

This study introduces an automated evaluation method for large language model (LLM) based chatbots in educational settings, utilizing LLM-as-a-Judge to assess their performance. Our results demonstrate the efficacy of this approach in evaluating the accuracy of three LLM-based chatbots (Llama 3 70B, ChatGPT 4, Gemini Advanced) across two subjects: history and biology. The analysis reveals promising performance across different subjects. On a scale from 1 to 5 describing the correctness of the judge itself, the LLM judge’s average scores for correctness when evaluating each chatbot on history related questions are 3.92 (Llama 3 70B), 4.20 (ChatGPT 4), 4.51 (Gemini Advanced); for biology related questions, the average scores are 4.04 (Llama 3 70B), 4.28 (ChatGPT 4), 4.09 (Gemini Advanced). This underscores the potential of leveraging the LLM-as-a-judge strategy to evaluate the correctness of responses from other LLMs.