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An Empirical Study on Factors Influencing User Adoption of AI-Enabled Chatbots for the Healthcare Disease DiagnosisSaram, Tharindu January 2024 (has links)
In healthcare, the rising demand for medical services, compounded by a shortage of professionals, presents significant challenges. To address these issues, the healthcare industry has turned to artificial intelligence (AI) to enhance various services such as disease diagnosis, medical imaging interpretation, clinical laboratory tasks, screenings, and health communications. By offering real-time, human-like interactions, AI-driven chatbots facilitate access to healthcare information and services, aiding symptom analysis and providing preliminary disease information before professional consultations. This initiative aims not only to reduce healthcare costs but also to enhance patient access to medical data. Despite their growing popularity, AI-enabled chatbots or conversational agents chatbots in the healthcare disease diagnosis domain continue to encounter obstacles such as a limited user adoption and integration into healthcare systems. This study addresses a gap in the existing literature on the adoption of AI enabled healthcare disease diagnosis chatbots by analysing the elements that influence users' behavioural intention to utilize AI-enabled disease diagnosis chatbots. Employing the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as a theoretical framework, this quantitative study began with exploratory research to define its scope and context, followed by a survey of 130 participants. The study utilized multiple linear regression and Pearson correlation analysis to evaluate the data. The outcomes suggest that performance expectancy, habits, social influence, and trust significantly associated with the individuals’ behavioural intentions to use AI-enabled chatbots for disease diagnosis. The results of this study reveal that performance expectancy, habits, social influence, and trust significant association with intention to use AI-enabled chatbots for disease diagnosis. The outcomes of this study contribute to existing knowledge in information systems, particularly identifying key factors that boost user adoption of AI-enabled chatbot applications for disease diagnosis. These insights can guide system designers, developers, marketers, and promotors involved in developing, revamping, and promoting chatbot applications, considering the influential factors discovered in this research, thereby increasing the usage of chatbot apps. Furthermore, the research model developed here could serve as a valuable model for future studies on disease diagnostic chatbot applications.
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Användarupplevelsen i AI-baserade applikationer : En kvalitativ studie av interaktion med AI-drivna chatbotar för att optimera användarupplevelsen / Enhancing User Experience in AI-powered Applications : A qualitative study of user interaction with AI-powered chatbots to optimise user experienceLeuchuk, Sviatlana January 2023 (has links)
Andelen av produkter med integrerad artificiell intelligens och tjänster som bygger på AI har ökat de senaste åren. Implementering och användning av tekniken är en snabbt växande marknad där tillväxten sker inom privata bolag och offentlig sektor. AI-verktyg lockar verksamheter för att kunna effektivisera processer, förbättra produkter och tjänster med hjälp av chatbotar som integreras i applikationer och webbplatser. En chatbot är en programvara som använder olika tekniker och modeller för att förstå och svara på frågor, simulera mänsklig konversation och hantera olika uppgifter. Trots alla fördelar som AI-drivna chatbotar medför, har vissa utmaningar framkommit. Användare har blivit mer tveksamma till de synliga och osynliga tillämpningarna av AI och hur de påverkar ens interaktioner och beslutsfattande. Det är viktigt för utvecklare och designers av AI-drivna chatbotar att ta hänsyn till användarnas upplevelser och reflektera över användarvänligheten under hela processen. Därför syftar denna studie till att undersöka användandet av gränssnitt i applikationer som är baserade på AI-drivna chatbotar för att identifiera faktorer som optimerar upplevelser ur ett människocentrerat perspektiv. Studiens fokus ligger på chatbotar som används i mobilapplikationer och webbplatser, och som integrerar med användare genom text i ett digitalt gränssnitt. Genom kvalitativa forskningsmetoder, intervjuer och observation, med teoretisk bakgrund från litteraturen, samlades information in om användares erfarenheter och preferenser. Undersökningen genomfördes med totalt sju deltagare, där fem slutanvändare utvärderade två gränssnitt i applikationer som bygger på AI-drivna chatbotar och två apputvecklare delade med sig av sina erfarenheter. Det resulterade i att datainsamlingen identifierade tolv kvalitativa faktorer inom fyra problemområden: kommunikation, mänsklig kontroll, transparens i systemfunktioner och visuell presentation, som har en påverkan på användarupplevelsen. Utifrån undersökningen har rekommendationer formulerats som beskriver vad som bör beaktas vid design av AI-baserade applikationer. Detta arbete bidrar till framtida arbete med utveckling av användbara applikationer baserade på artificiell intelligens. / Implementing artificial intelligence in products and services has increased significantly in recent years. AI tools, such as chatbots integrated into applications and websites, have become popular among companies to simplify work-related tasks and improve user satisfaction. “Chatbot” is software that uses different techniques and models to understand and answer users' questions, simulate human conversation and handle various tasks. However, users have become more doubtful about AI using implicit and explicit in products and services and how it affects interaction and decision-making. It is important that developers and designers of AI-powered chatbots consider user experiences and reflect on the usability and user experience of the chatbots. Therefore, this work aims to investigate the use of interfaces in applications based on AI-powered chatbots to identify factors that optimise the user experience. To reach this purpose, the researcher of this bachelor thesis collected information about users' experiences and preferences through qualitative research methods, interviews and observation. The research involved seven participants, two developers sharing their expertise, and five users evaluating two chatbots where users interacted with chatbots through text in a digital interface. As a result, the collected data identified twelve qualitative factors within four problem areas: communication, human control, transparency in system functions and visual presentation, which impact the user experience. This study suggests recommendations considering these factors that can increase user satisfaction and contribute to future work in developing applications based on artificial intelligence.
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