ChatGPT is a newly launched Artificial Intelligence (AI) powered model with several functions, providing the user with human-like responses. Recently, ChatGPT have gain a lot of recognition and popularity. The aim of this research is to examine the perceptions of ChatGPT from a Swedish professional service workers (PSW) perspective. More precisely, the study explores how the usage of ChatGPT in regard to supporting various work tasks is perceived. Additionally, the aim is to examine what factors could influence the perceptions regarding the model, and how the information provided is viewed by PSWs. The research uses a qualitative approach, and the data is collected through semi-structured interviews. Moreover, the study uses a thematic analysis for the analysis of data gathered. Additionally, the study uses Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the factors influencing PSWs perception of ChatGPT. The findings show that PSWs believed that ChatGPT could be used to support some of their work tasks. The model was seen as easy to use and had its benefits, such as perceived increased productivity and efficiency. However, the findings also indicate that there are several challenges that could influence the overall usage of ChatGPT. Overall, both performance expectancy and effort expectancy showed to be important factors of the evaluation of ChatGPT usage in this study. Moreover, the findings indicate that the functions and information provided by ChatGPT could influence the perceptions. For example, lack of references, lack of human touch, and security issues were found to influence the interviewed PSWs. Additionally, the study concludes that there are several perceived areas of improvements regarding ChatGPT. This research contributes with knowledge about ChatGPT from a PSWs perspective and how it could be used for work related tasks.

Denna studie undersöker de retoriska strategierna som används i debattartiklar publicerade av den svenska tidningen Svenska Dagbladet angående användningen av ChatGPT, en avancerad chatbot, inom utbildning. Analyserna av dessa artiklar syftar till att belysa argumenten för och emot ChatGPT:s användning i skolor och bidra till en bättre förståelse av hur retorik kan forma åsikter om teknologi och innovation inom utbildning. För att uppnå detta använder studien en teoretisk ram som hämtar från konstruktivismteori, medialisering, moralpanik, teknisk determinism och retorikteori och dess övertygande enheter. Forskningsfrågorna som utforskas i denna studie inkluderar skildringen av ChatGPT:s påverkan på utbildning, de retoriska strategierna som används i artiklarna och den potentiella påverkan av moralisk panik på attityder till teknologi och innovation inom utbildning. Resultaten avslöjar att artiklarna presenterade både positiva och negativa perspektiv på användningen av ChatGPT och att olika retoriska strategier, såsom appel till auktoritet och känslor, användes för att påverka läsarnas åsikter. Studien bidrar till en bättre förståelse av retorikens roll i att forma attityder till användningen av avancerade chatbots inom utbildning och betonar vikten av att överväga flera teoretiska perspektiv vid analys av mediediskurs. / This study examines the rhetorical strategies used in opinion articles published by the Swedish newspaper Svenska Dagbladet regarding the use of ChatGPT, an advanced chatbot, in education. The analysis of these articles aims to shed light on the arguments for and against ChatGPT's use in schools and to contribute to a better understanding of how rhetoric can shape opinions about technology and innovation in education. To accomplish this, the study employs a theoretical framework that draws from constructivism theory, mediatization, moral panic, technological determinism, and rhetoric theory and its persuasive devices. The research questions explored in this study include the portrayal of ChatGPT's impact on education, the rhetorical strategies used in the articles, and the potential influence of moral panic on attitudes towards technology and innovation in education. The findings reveal that the articles presented both positive and negative perspectives on the use of ChatGPT, and that various rhetorical strategies, such as appeals to authority and emotion, were used to influence readers' opinions. The study contributes to a better understanding of the role of rhetoric in shaping attitudes towards the use of advanced chatbots in education and highlights the importance of considering multiple theoretical perspectives in analyzing media discourse.

Digitalisering är under ständig utveckling inom banksektorn. I och med den växande utvecklingen så har intresset för artificiell intelligens ökat men trots detta finns det fortfarande kvar osäkerheter kring användandet av AI-chatbots. Banker hanterar en stor mängd känsliga uppgifter vilket gör att konsumenters upplevda oro och tillit är extra känsligt. Detta är viktigt att understryka då tidigare forskning har visat att tillit utgör en stor del av konsumenters beslut att använda AI-chatbot. Baserat på tidigare forskning har denna studie knutit samman teorier om vilka faktorer som påverkar svenska bankkonsumenters tillit till AI chatbots samt vilka faktorer som påverkar svenska bankkonsumenters intention att använda AI chatbots vid utförande av bankärenden online. Detta utgör även studiens syfte. Det teoretiska ramverket i studien utgörs av technology acceptance model (TAM), on-line trust-modellen och upplevd mänsklighet. Utifrån dessa teorier har studiens hypoteser byggts upp där vardera hypotes testar en faktor som beskrivs i de valda teorierna. Datainsamlingen skedde genom en enkätundersökning online som distribuerades på sociala kanaler. Undersökningen genererade 87stycken fullständiga svar. Insamlade data analyserades med hjälp av Minitab och Excel. De analysmetoder som använts för att analysera insamlade data var huvudsakligen korrelationstest och regressionsanalys. Resultatet visade att den upplevda nyttan och tillit är faktorer som påverkar intentionen till användning positivt. Resultatet visade även att faktorerna trovärdighet och användarvänlighet påverkar tilliten positivt samt att risk har en negativ påverkan på tilliten. För att effektivisera användning av AI-chatbots som kundtjänstverktyg så rekommenderas svenska banker att fokusera på att öka den upplevda nyttan i form av att säkerställa snabbhet, produktivitet och enkelhet. Svenska banker bör även säkerställa att bankkonsumenter känner tillit till AIchatbots. Detta kan göras genom att fokusera på ökad användarvänlighet och trovärdighet samtidigt som banker bör minska den upplevda risken i form av integritetsrisk då detta är faktorer som visat samband med tillit till AI-chatbots inom banksektorn. / Digitalization is under constant development within the banking industry. With this comes a growing interest in artificial intelligence. Despite this, uncertainty regarding the use of AI-chatbots remains. Banks handle a large amount of sensitive information which, for the consumers, makes the perceived trust especially delicate. Earlier research has shown that trust has a significant impact on the consumers intention to use AI-chatbots. Based on previous research this study aims to tie together theories about which factors impact Swedish bank customers’ trust towards AI-chatbots, as well as the customers’ intention to use AI-chatbots while performing bank-related business online. This is also considered to be the purpose of this paper. In this study the theoretical standpoint is based on the technology acceptance model (TAM), on-line trust and perceived humanness. From these well-established theories a set of hypotheses has been presented, where each hypothesis examines different factors that are presented in the used model. The data collection in this study was conducted through an online survey, where a total of 87 answers were collected. The data was analyzed using Minitab. The main analysis methods used were correlation tests as well as a regression analysis. The results showed that the perceived usefulness and trust are factors that have a positive impact on the intention to use AI-chatbots while performing banking tasks. The results also showed that ease of use and credibility have a positive impact on trust while risk has a negative impact on trust toward AI-chatbots. To make the use of AI-chatbots more effective as a customer service tool this study recommends Swedish banks to focus on increasing the perceived usefulness of the AI-chatbots. This can be done by ensuring the AI-chatbots are able to provide productivity, simplicity and swiftness to its users. Swedish banks should also focus on increasing customers’ perceived trust in the AIchatbots. This can be achieved by increasing the ease of use as well as the credibility of the system. To decrease the experienced risk of using the AI-chatbots, this study recommends banks to focus on decreasing the integrity risk.

Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECA's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.

Chattbotutvecklare på Softronic använder i dagsläget Rasa-ramverket och dess standardkomponenter för bearbetning av användarinmatning. Det här är problematiskt då standardkomponenterna inte är optimerade för det svenska språket. Till följd av detta efterfrågades en utvärdering av samtliga Rasa-komponenter med syfte att identifiera de mest gynnsamma komponenterna för att maximera klassificeringsträffsäkerhet. I detta examensarbete framtogs och jämfördes flera Rasa-pipelines med olika komponenter för tokenisering, känneteckensextrahering och klassificering. Resultaten av komponenterna för tokenisering visade att Rasas WhitespaceTokenizer överträffade både SpacyTokenizer och StanzaTokenizer. För känneteckensextrahering var CountVectorsFeaturizer, LanguageModelFeaturizer (med LaBSE-modellen) och FastTextFeaturizer (med den officiella fastText-modellen tränad på svenska Wikipedia) de mest optimala komponenterna. Den klassificerare som i allmänhet presterade bäst var DIETClassifier, men det fanns flera tillfällen där SklearnIntentClassifier överträffade den.   Detta arbete resulterade i flera pipelines som överträffade Rasas standard-pipeline. Av dessa pipelines var det två som presterade bäst. Den första pipeline implementerade komponenterna WhitespaceTokenizer, CountVectorsFeaturizer, FastTextFeaturizer (med den officiella fastText-modellen tränad på svenska Wikipedia) och DIETClassifier med en klassificeringsträffsäkerhet på 91% (F1-score). Den andra pipeline implementerade komponenterna WhitespaceTokenizer, LanguageModelFeaturizer (med LaBSE-modellen) och SklearnIntentClassifier med en klassificeringsträffsäkerhet på 91,5% (F1-score). / Chatbot developers at Softronic currently use the Rasa framework and its default components for processing user input. This is problematic as the default components are not optimized for the Swedish language. Following this an evaluation of all Rasa components was requested with the purpose of identifying the most favorable components to maximize classification accuracy. In this thesis, several Rasa pipelines were developed and compared with different components for tokenization, feature extraction and classification. The results of the tokenization components showed that Rasa's WhitespaceTokenizer surpassed both SpacyTokenizer and StanzaTokenizer. For feature extraction, CountVectorsFeaturizer, LanguageModelFeaturizer (with the LaBSE model) and FastTextFeaturizer (with the official fastText model trained on Swedish Wikipedia) were the most optimal components. The classifier that generally performed best was DIETClassifier, but there were several occasions where SklearnIntentClassifier surpassed it. This work resulted in several pipelines that exceeded Rasa’s standard pipeline. Of these pipelines, two performed best. The first pipeline implemented the components WhitespaceTokenizer, CountVectorsFeaturizer, FastTextFeaturizer (with the official fastText model trained on Swedish Wikipedia) and DIETClassifier with a classification accuracy of 91% (F1 score). The other pipeline implemented the components WhitespaceTokenizer, LanguageModelFeaturizer (with the LaBSE model) and SklearnIntentClassifier with a classification accuracy of 91.5% (F1 score).

Syftet med denna studie är att undersöka hur användningen av AI-chatbotar hos Amazon påverkar kundnöjdheten. Metodvalet för denna studie är kvantitativ och baseras på data insamlad genom en enkätundersökning. Enkäten är utformad för att kvantifiera och analysera respondenternas åsikter, beteenden och demografiska variabler. Data analyseras med hjälp av SEM och AMOS. Studien har identifierat signifikanta samband mellan förtroende och kundnöjdhet samt uppfattad enkelhet och kundnöjdhet när det gäller Amazons AI-chatbot. Sambandet är positivt och ökad förtroende samt uppfattad enkelhet leder till ökad kundnöjdhet. / The purpose of this study is to investigate how the use of AI chatbots at Amazon affects customer satisfaction. The method chosen for this study is quantitative and is based on data collected through a survey. The survey is designed to quantify and analyze respondents' opinions, behaviors, and demographic variables. The data is analyzed using SEM and AMOS. The study has identified significant relationships between trust and customer satisfaction, as well as perceived simplicity and customer satisfaction regarding Amazon´s AI chatbot. The relationship is positive and increased trust and perceived ease of use leads to increased customer satisfaction.

Titel: Redovisningsstudenter & generativ AI  Nivå: Examensarbete på grundnivå (kandidatexamen) i ämnet företagsekonomi. Författare: Jennifer Roos och Josefine Olsson Handledare: Jan Svanberg Datum: 2024 – maj Syfte: Undersöka hur redovisningsstudenter med olika inlärningsstrategier (ytinlärning och djupinlärning) använder generativ AI i sina studier samt att analysera hur generativ AI bidrar till studenternas lärande.    Metod: Studien utgår från en positivistisk forskningsfilosofi och en deduktiv forskningsansats. Metoden består av en kvantitativ forskningsdesign med en tvärsnittsdesign i form av en enkätundersökning som utformar studiens primärdata bestående av 62 respondenter, varav 10 respondenter uteslöts och räknas som bortfall. Datamaterialet har kodats och analyserats i statistikprogrammet SPSS.   Resultat och slutsats: Studiens resultat indikerar att det finns en jämn spridning mellan inlärningsstrategierna yt- och djupinlärning hos redovisningsstudenter samt att fåtalet redovisningsstudenter tillhör båda inlärningsstrategierna. Resultatet visar att generativ AI kan användas i både ytinlärning och djupinlärning och tenderar att accentuera den aktuella inlärningsstrategin.    Examensarbetes bidrag: Studien bidrar med ny, högaktuell och viktig forskning till forskningsgapet gällande hur generativ AI påverkar redovisningsstudenters inlärningsstrategi. Insikterna från studien bidrar till en ökad förståelse kring utformningen av redovisningsutbildningen för att förbereda redovisningsstudenter inför yrket.   Förslag till fortsatt forskning: Framtida forskning kan utöka urvalet för att bättre representera populationen, redovisningsstudenter. Dessutom bör framtida forskning utforska hur andra inlärningsstrategier kan påverka användningen av generativ AI samt undersöka samband mellan variabler som kön, ålder, geografisk plats och kursämne för att identifiera likheter, skillnader och mönster.    Nyckelord: Chatbotar, Djupinlärning, Generativ AI, Inlärningsstrategier, Redovisningsstudenter & Ytinlärning. / Title: Accounting Students & generative AI  Level: Student thesis, final assignment for Bachelor Degree in Business Administration. Author: Jennifer Roos and Josefine Olsson Supervisor: Jan Svanberg Date: 2024 – May                                                     Aim: To investigate how accounting students with different learning strategies (surface learning and deep learning) use generative AI in their studies and to analyze how generative AI contributes to students’ learning.    Method: The study is based on a positivist research philosophy and a deductive research approach. The method is a quantitative research design with a cross-sectional design in the form of a questionnaire that forms the study's primary data consisting of 62 respondents, of which 10 respondents were excluded and counted as non-valid. The data has been coded and analyzed in the statistical program SPSS.   Results and conclusions: The results of the study indicate that there is an even spread between the learning strategies, surface- and deep learning, in accounting students and that the few accounting students belong to both learning strategies. The result shows that generative AI can be used for both surface learning and deep learning and tends to accentuate the current learning strategy.   Contribution of the thesis: The study contributes to new, highly current and important research to the research gap regarding how generative AI affects the learning strategy of accounting students. The insights from the study contribute to an increased understanding of the design of accounting education to prepare accounting students for the profession.   Suggestions for future research: Future research could expand the sample to better represent the population, accounting students. Additionally, future research should explore how other learning strategies may influence the use of generative AI as well as examine relationships between variables such as gender, age, geographic location, and course subject to identify similarities, differences, and patterns. Key words: Accounting Students, Chatbots, Deep learning, Generative AI, Learning strategies & Surface learning.

Background: Chatbots have developed from simple algorithmic tools to advanced systems that automate and personalise B2B customer interactions, therefore improving CRM through the efficiency of service and customer satisfaction. Purpose: The purpose of this thesis is to explore the potential of chatbots as a solution to address the challenges faced by B2B customer service in today's business environment. Method: This chapter explains the utilisation of semi-structured interviews in relation to discovering challenges and later on the possible chatbot solutions for B2B customer service. It includes details on the selection of participants, data collection, and methods for data analysis. Findings: The study explores challenges and strategies in B2B customer service, where high inquiry volumes, after-hours contact, and the need for more staff are prominent issues. Factors for quality service include time, staff training, and efficient communication channels. Trust and consistency are upheld through internal communication tools and CRM technology. The participants commented on the need for quick responses and discussed the future use of technologies like chatbots. Conclusion: This thesis focuses on how chatbots can enhance B2B customer service through efficient handling of high-volume inquiries, ensuring GDPR compliance, and balancing automated responses with personalised engagement. These capabilities further service responsiveness and personalization, leading to increasing customer satisfaction and loyalty in B2B environments.

Interaction with digital products and interfaces concern more and more of human decision-making and the problems regarding environmental, financial and social sustainability are consequences much due to our behavior. The issues and goals of sustainable development therefore implies how we have to think differently about digital design. In this paper, we examine the adequacy of influencing sustainable behavior with a data-driven design approach, applying a conversational user interface. A case study regarding the United Nation’s goals of technological development and economic distribution was conducted, to see if a hypothetical business with a proof-of-concept digital product could be effective in influencing where companies base their operations. The test results showed a lack of usability and influence, but still suggested a potential with language-based interfaces. Even though the results could not prove anything, we argue that leveraging data analysis to design for sustainable behavior could be a very valuable strategy. A data-driven approach could enable ambitions of profit and user experience to coincide with those of sustainability, within a business organization.

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.