LEADER 03512nam 22005775 450 001 9911040915703321 005 20251108120400.0 010 $a3-658-50136-7 024 7 $a10.1007/978-3-658-50136-5 035 $a(MiAaPQ)EBC32406626 035 $a(Au-PeEL)EBL32406626 035 $a(CKB)42349145500041 035 $a(DE-He213)978-3-658-50136-5 035 $a(EXLCZ)9942349145500041 100 $a20251108d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI-Driven Mental Health Chatbots $ePerceived Empathy, User Satisfaction and Treatment Outcomes /$fby Lynn Miriam Weisker 205 $a1st ed. 2025. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Gabler,$d2025. 215 $a1 online resource (109 pages) 225 1 $aBestMasters,$x2625-3615 311 08$a3-658-50135-9 327 $aIntroduction -- Research Gap -- Research Background -- Research Design -- Results -- Discussion -- Conclusion -- Limitations and Future Research Directions. 330 $aAs artificial intelligence (AI) continues to evolve, its potential role in online mental health therapy is gaining increasing interest. In this study, a quantitative 2x2 factorial experimental design is used to explore how AI transparency, theory of change (ToC), therapy style of advice, AI acceptance rate and type of mental health issue influence user perceptions of AI-driven mental health chatbots. Using a mixed-methods approach that combines quantitative analysis with sentiment and emotional text mining, the research examines how these variables shape user experiences in terms of perceived empathy, satisfaction and treatment outcomes. The findings reveal that participants who are aware they are interacting with AI tend to report more positive experiences, particularly when an emotional ToC is employed. Furthermore, emotional advice styles elicit deeper emotional engagement, while rational advice is associated with more positive sentiment. Additionally, the emotional tone and conversational dynamics vary by discussion topic, with depression-related conversations showing greater emotional intensity. These insights underline the importance of aligning chatbot communication styles with individual user expectations and emotional needs, offering implications for the design of more personalised mental health technologies. About the Author Lynn Miriam Weisker is a master's student at the Department of Information Systems at the University of Liechtenstein. Her research focuses on AI-supported mental health chatbots and their use in supporting mental health. 410 0$aBestMasters,$x2625-3615 606 $aTechnological innovations 606 $aComputer science 606 $aArtificial intelligence 606 $aInnovation and Technology Management 606 $aComputer Science 606 $aArtificial Intelligence 615 0$aTechnological innovations. 615 0$aComputer science. 615 0$aArtificial intelligence. 615 14$aInnovation and Technology Management. 615 24$aComputer Science. 615 24$aArtificial Intelligence. 676 $a658.4062 676 $a658.514 700 $aWeisker$b Lynn Miriam$01856568 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911040915703321 996 $aAI-Driven Mental Health Chatbots$94456138 997 $aUNINA