Digital Warmth? The Influence of AI- and Therapist Feedback on Common Factors in Digital Mental Health Interventions

Digital mental health interventions (DMHIs) offer scalable ways to support people affected by mental health problems. Their usage and effectiveness can be enhanced by additional written feedback (so-called guidance). Such feedback can enhance engagement by fostering core psychotherapeutic mechanisms (so-called common factors) like expectations or the therapeutic alliance. Traditionally, human therapists provided this feedback, but artificial intelligence (AI), more specifically large language models (LLMs), may offer a more cost-effective and time-saving alternative. The central research question of this project is: Can AI feedback feel as supportive and instill hope for therapeutic change as human feedback? In a 2×2 vignette design, we vary feedback source (therapist vs. AI) and transparency (blinded vs. unblinded) to explore how these factors shape the perception of common factors in DMHI feedback in a sample of laypeople.