Användning av AI har ökat i skolans värld, och med ökningen av användandet följer viss osäkerhet och kritik kring ämnet. En del av problematiken ligger i korrektheten och pålitligheten hos AI:n, medan annan problematik ligger i hur tillgången till AI ser ut mellan olika skolor, då detta kan skapa orättvisor. Ytterligare en aspekt där problematik kan uppstå är kring elevers integritet och säkerhet.  För att belysa ämnet AI i skolor har denna studie genomförts i syfte att identifiera och beskriva de för- och nackdelar som AI användningen kan ha på skolväsendet utifrån ett gymnasienivå perspektiv. Samt besvara frågan om användningen av ChatGPT kan vara ett stöd eller ett hinder för undervisningen, utifrån gymnasielärares perspektiv. Datainsamling har skett genom teoretisk litteraturstudie samt semistrukturerade intervjuer av fem gymnasielärare. Analysmetoden som har använts är en tematisk innehållsanalys.    Resultatet av insamlat material klargör för- och nackdelar gällande användandet av AI på gymnasiet. Några av fördelarna som synliggjordes var att AI skapar möjligheten till individanpassad utbildning, kan komplettera befintligt studiematerial och ge en sammanfattande beskrivning av utvalt material. Några av nackdelarna som uppmärksammades var att materialet som genereras av ChatGPT eller andra AI-verktyg saknar ansvarig utgivare och kan skapa möjligheten för eleverna att fuska.   Studiens slutsatser är att ChatGPT är ett bra utbildningsstöd om verktyget används på ett etiskt sätt. Skolverket med andra akademiska intuitioner måste ta fram riktlinjer och policyer som både elever och lärare kan ta del av och följa. Slutligen bör utbildning erbjudas till såväl lärare som elever på hur ChatGPT kan användas som ett studiestöd i stället för ett hinder.

aus der Einführung und Motivation: Unser Beitrag stellt eine webbasierte modellgesteuerte Umgebung für soziale Bots vor mit der wir CoPs die Möglichkeit geben, ihre eigenen Lernassistenten zu erstellen. Unser Ansatz kann verwendet werden, um soziale Bots für die eigene Organisation zu erstellen und auf Webdienste zurückgreifen kann, indem man sich auf RESTful APIs it OpenAPI-Spezifikation verlässt, um die Aktionen des sozialen Bots innerhalb der Anwendung zu definieren. Abschließend werden drei Beispiele von Mentoring Bots vorgestellt, die mit dem Framework erstellt wurden und zeigen das große Potential der Anwendung. Mit den Bots erhalten Studierenden personalisiertes Feedback, ohne dass die Lehrkräfte zusätzlich belastet werden.

Virtual humans and other virtual agents are becoming more common in our everyday lives. Whether in the form of phone-based personal assistants or automated customer service systems, these technologies have begun to touch more of our activities. This research aims to understand how this technology affects the way we interact with our computer systems. Using a chat bot, I studied the way a conversational computer system affects the way people interact with and perceive automated interviewing systems in two different contexts. Study 1 examines the impact of a conversational agent on behavior during deception. It found that a conversational agent can have a powerful impact on people's perception of the system, resulting in individuals viewing the system as much more engaging and human. The conversational agent further results in a suppression of deception-related cues consistent with a more human-like interaction. Study 2 focuses on the effect of a conversational agent on socially desirable responding. Results of this study indicate that a conversational agent increases social desirability when the topic of the interview is sensitive, but has no effect when the questions are non-sensitive. The results of these two studies indicate that a conversational agent can change the way people interact with a computer system in substantial and meaningful ways. These studies represent a step toward understanding how conversational agents can shape the way we view and interact with computers.

“MeatSpace” is a group of related bodies of work including podcasts, prints and videos produced by working with simulation technologies such as “weak A.I.”, virtual reality, and 3d scans. Collectively the works explore how people relate to these technologies and how they relate to us. They share thematic or process-oriented sensibilities involving a series of rule-based steps that alternate between the procedural and the intentional. “Uncanny” is defined as strangely familiar. Something which falls in the Uncanny Valley feels wrong, but the reasons may be difficult to articulate. “MeatSpace” is an ongoing experiment to see where our own digital reflections fall in an uncanny spectrum of unsettling familiarity. I am continuously assessing the meaning of words like “consciousness" and “choice”, wondering if they are simply interpretations of randomness and determinism. So I search for genuine glimmers of agency in technologies from the present and the past to better understand my own. The groupings within “MeatSpace” are titled “The Intrinsia Chatbox” (podcast), “Outside the Chatbox” (podcast), “Sweet Space/Spatial Awareness”(prints, video, augmented reality), “Formulaics”(music videos), and “Texture Maps/Morph Maps” (prints, video). “MeatSpace” uses manipulation of photogrammetry, volumetric video capture, procedural music generation, animations made in virtual reality, and a video podcast which showcases conversations with chatbots. I see these as acts of collaboration and play with the digital world and it’s developing tools and inhabitants. These processes employ both randomness and control, operating between meatspace and the digital world, between comfort and the uncanny valley.

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BACKGROUND: Most women within reproductive ages living in Low- and Middle-Income Countries, have limited or no access to education on sexual and reproductive health and contraception, yet about 1.9 billon women in LMICs own a mobile phone. The exposure to digital health services has the potential to contribute to improving awareness, influencing positive perceptions, beliefs and promoting SRH and contraception.AIM: To explore user views and perceptions on the Grace Health’s (a digital health service) ability to improve awareness on sexual reproductive health and contraception among Ghanaian, Nigerian and Kenyan women aged 18-35.METHODOLOGY: A qualitative design using phenomenography to interview women remotely on women’s perception of digital health services through their own experience with using the Grace health chat bot and app. Also, their views were sought on how other women are gaining awareness and impacts on sexual reproductive health and contraception from the digital health services.RESULTS: Seven categories on digital health services emerged, with key findings presented as Safe days, ovulation as contraception, impacts on SRH, Avoiding or seeking pregnancy and influence on perception.CONCLUSION: This qualitative study gives insight for research community, public health professionals, app designers, health care providers, stakeholders and civil society organisations in making decisions regarding the use of digital health service as strategic, innovative instruments for interventions in major key indicators of Sexual Reproductive Health and Rights.

Recently a new method for training generative neural networks called Generative Adversarial Networks (GAN) has shown great results in the computer vision domain and shown potential in other generative machine learning tasks as well. GAN training is an adversarial training method where two neural networks compete and attempt to outperform each other, and in the process they both learn. In this thesis the effectiveness of GAN training is tested on conversational agents also called chat bots. To test this, current state-of-the-art training methods such as Maximum Likelihood Estimation (MLE) models are compared with GAN method trained models. Model performance was measured by closeness of the model distribution from the target distribution after training. This thesis shows that the GAN method performs worse the MLE in some scenarios but can outperform MLE in some cases. / Nyligen har en ny metod för att träna generativa neurala nätverk kallad Generative Adversarial Networks (GAN) visat bra resultat inom datorseendedomänen och visat potential inom andra maskininlärningsområden också GAN-träning är en träningsmetod där två neurala nätverk tävlar och försöker överträffa varandra, och i processen lär sig båda. I detta examensarbete har effektiviteten av GAN-träning testats på konversationsagenter, som också kallas Chat bots. För att testa det här jämfördes modeller tränade med nuvarande state-of- the-art träningsmetoder, så som Maximum likelihood-metoden (ML), med GAN-tränade modeller. Modellernas prestation mättes genom distans från modelldistribution till måldistribution efter träning. Det här examensarbetet visar att GAN-metoden presterar sämre än ML-metoden i vissa scenarier men kan överträffa ML i vissa fall.

This thesis explores the idea of how people 'do gender' in their online use of avatars, specifically avatar choice. A secondary question of whether or not a chatterbot can be used as a potential interviewer will also be examined as a tool acquiring large amounts of interview data. Gender is one of the ways in which we structure our society, and is completely omnipresent. We cannot opt out of participating in our gender, as we are constantly performing and reaffirming it. Because of this, gender performance and choice spills over into all domains. This includes entertainment such as massively multiplayer online games, both in how the designers make the game, and what the players bring to the game. Deconstructing how and why people engage in these gendered practices and choices becomes an interesting avenue of research, because it allows researchers to partially separate the mental aspects of gender from physical attributes, as the players' physical bodies are not actually in the game. Through the lens of the popular massively multiplayer online game, World of Warcraft, this thesis will utilize a qualitative user research study to understand how gender affects avatar choices. Prior research identified areas where players brought real world gender norms into the games they played. This research study will extend previous research by having players identify why they made the choices they made for their avatars, and how they feel about those choices. The methodology for this study will also involve using a chatterbot as a way of gathering interviews. In normal person-to-person interview studies, recruiting and organizing meetings for these interviews can often be a difficult task. This thesis brings in the idea of using a chatterbot as a mechanism to gather more interviews in a shorter time span to alleviate the problem of getting these one-on-one interviews in some types of studies.

Language is critical to establishing long-term cooperative relationships among intelligent agents (including people), particularly when the agents' preferences are in conflict. In such scenarios, an agent uses speech to coordinate and negotiate behavior with its partner(s). While recent work has shown that neural language modeling can produce effective speech agents, such algorithms typically only accept previous text as input. However, in relationships among intelligent agents, not all relevant context is expressed in conversation. Thus, in this paper, we propose and analyze an algorithm, called Llumi, that incorporates other forms of context to learn to speak in long-term relationships modeled as repeated games with cheap talk. Llumi combines models of intentionality with neural language modeling techniques to learn speech from data that is relevant to the agent's current context. A user study illustrates that, while imperfect, Llumi does learn context-aware speech repeated games with cheap talk when partnered with people, including games in which it was not trained. We believe these results are useful in determining how autonomous agents can learn to use speech to facilitate successful human-agent teaming.

Accurately tracking employee work hours is a vital aspect of managing any organization, regardless of its size. Time reporting serves as a valuable source of information for management teams, enabling them to effectively plan and manage projects, allocate resources, monitor budgets, and track overall productivity. It is an essential component of workforce management that assists companies in making informed decisions regarding scheduling, employee compensation, and resource allocation. Time reporting is an important task for companies. However, the process of time reporting can often be cumbersome and time-consuming for employees. Typically, employees are required to log in to a web application and manually input their work hours, vacation days, and sick leaves. This process can be complicated and prone to errors. To simplify this process, an innovative solution was developed using Information Technology. The solution involved the creation of a Slack bot, which is a chat-bot that can communicate with employees and gather time reporting data automatically. This bot can be integrated with a company’s existing communication tools, allowing employees to report their work hours, vacation days, and sick leaves in real-time using a simple chat interface. The bot can also provide reminders to employees to submit their time reports, reducing the chances of delayed submissions. / Att noggrant spåra anställdas arbetstid är en viktig aspekt av att leda alla organisationer, oavsett dess storlek. Tidsrapportering fungerar som en värdefull informationskälla för ledningsgrupper, vilket gör det möjligt för dem att effektivt planera och hantera projekt, allokera resurser, övervaka budgetar och spåra den totala produktiviteten. Det är en viktig komponent i personalledning som hjälper företag att fatta välgrundade beslut om schemaläggning, ersättning till anställda och resursallokering. Tidsrapportering är en viktig uppgift för företag. Processen med tidsrapportering kan dock ofta vara krånglig och tidskrävande för de anställda. Vanligtvis måste anställda logga in på en webbapplikation och manuellt mata in sina arbetstimmar, semesterdagar och sjukskrivningar. Denna process kan vara komplicerad och risk för fel. För att förenkla denna process utvecklades en innovativ lösning med hjälp av informationsteknologi. Lösningen innebar skapandet av en Slack-bot, som är en chat-bot som kan kommunicera med anställda och samla in tidsrapporteringsdata automatiskt. Denna bot kan integreras med ett företags befintliga kommunikationsverktyg, så att anställda kan rapportera sina arbetstimmar, semesterdagar och sjukskrivningar i realtid med hjälp av ett enkelt chattgränssnitt. Boten kan också ge påminnelser till anställda att skicka in sina tidsrapporter, vilket minskar risken för försenade inlämningar.