AI-Driven Mental Health Chatbots
Perceived Empathy, User Satisfaction and Treatment Outcomes
- 2025
- Buch
- Verfasst von
- Lynn Miriam Weisker
- Buchreihe
- BestMasters
- Verlag
- Springer Fachmedien Wiesbaden
Über dieses Buch
Da sich künstliche Intelligenz (KI) weiterentwickelt, gewinnt ihre potenzielle Rolle in der Online-Therapie psychischer Gesundheit zunehmend an Interesse. In dieser Studie wird ein quantitatives faktorielles experimentelles 2x2-Design verwendet, um zu untersuchen, wie KI-Transparenz, Theorie des Wandels (ToC), Therapiestil der Beratung, Akzeptanz von KI und Art des Problems der psychischen Gesundheit die Wahrnehmung der Benutzer von Chatbots zur künstlichen Intelligenz beeinflussen. Mithilfe eines methodengemischten Ansatzes, der quantitative Analyse mit Sentiment und emotionalem Text Mining kombiniert, untersucht die Studie, wie diese Variablen die Erfahrungen der Nutzer in Bezug auf Empathie, Zufriedenheit und Behandlungsergebnisse beeinflussen. Die Ergebnisse zeigen, dass Teilnehmer, die sich bewusst sind, dass sie mit KI interagieren, tendenziell positivere Erfahrungen machen, insbesondere wenn ein emotionales ToC verwendet wird. Darüber hinaus führen emotionale Beratungsstile zu einem tieferen emotionalen Engagement, während rationale Beratung mit positiveren Gefühlen verbunden ist. Zusätzlich variieren der emotionale Ton und die Gesprächsdynamik je nach Gesprächsthema, wobei depressionsbedingte Gespräche eine größere emotionale Intensität aufweisen. Diese Erkenntnisse unterstreichen die Bedeutung der Abstimmung der Kommunikationsstile von Chatbots auf die individuellen Erwartungen und emotionalen Bedürfnisse der Nutzer und bieten Implikationen für das Design personalisierterer Technologien für psychische Gesundheit.
Mit KI übersetzt
Über dieses Buch
As 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.
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Inhaltsverzeichnis
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Frontmatter
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1. Introduction
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
2. Research Gap
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
3. Research Background
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
4. Research Design
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
5. Results
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
6. Discussion
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
7. Conclusion
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
8. Limitations and Future Research Directions
Lynn Miriam WeiskerAbstractAs artificial intelligence (AI) advances, its role in online mental health therapy is attracting growing attention. This study uses a quantitative 2x2 factorial design to examine how AI transparency, theory of change (ToC), therapy advice style, AI acceptance, and type of mental health issue affect user perceptions of AI-driven mental health chatbots. Combining quantitative analysis with sentiment and emotional text mining, the research explores how these factors shape perceived empathy, satisfaction, and treatment outcomes. Results show that users aware of interacting with AI report more positive experiences, especially with an emotional ToC. Emotional advice fosters deeper engagement, while rational advice generates more positive sentiment. Emotional tone and dynamics also differ by topic, with depression discussions showing greater intensity. These findings highlight the need to tailor chatbot communication to user expectations and emotional needs, informing the design of more personalised mental health technologies. -
Backmatter
- Titel
- AI-Driven Mental Health Chatbots
- Verfasst von
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Lynn Miriam Weisker
- Copyright-Jahr
- 2025
- Electronic ISBN
- 978-3-658-50136-5
- Print ISBN
- 978-3-658-50135-8
- DOI
- https://doi.org/10.1007/978-3-658-50136-5
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