The Need and Presence of Core Therapeutic Elements in Artificial Intelligence Mental Health Chatbots in Comparison to Human Therapists: A Mixed-method Approach

Abstract Background: Artificial intelligence (AI) has made significant strides in mental health support, yet its ability to replicate core therapeutic qualities remains limited. Aim: This study aims to explore the need and presence of Rogerian therapeutic attributes of empathy, unconditional positive regard (UPR) and genuineness in human therapists and AI mental health chatbots. Design: This research employed mixed-method approach to compare Rogerian qualities in single-session therapy of young adults ( N = 60) with AI/human therapists. Materials and Methods: A convenience sampling method was employed to recruit participants. Participants’ perceptions of the therapeutic experience were measured using eighteen Likert-scale items (Cronbach’s α = 0.926) based on the core therapeutic attributes of empathy, UPR, and genuineness. Qualitative insights were obtained from open-ended questions. Statistical Analysis: Quantitative data were analyzed for frequencies, Chi-square, and Multivariate analysis of variance using SPSS 21. For qualitative analysis, QDA Miner Lite was used to generate codes and themes and perform Agglomerative Hierarchical Cluster Analysis. Results and Conclusion: Findings indicate that participants perceived human counselors as significantly more empathic and genuine than AI counselors. No differences were reported for UPR. Participants evaluated human and AI therapists along two distinct dimensions: relational depth, which strongly favored human therapists, and functional accessibility, which favored AI. Exposure to AI counseling appeared to increase participants’ willingness to consider AI as a preferred therapeutic modality, although human therapists remained the preferred choice overall. The findings underscore AI’s potential as a complementary tool for low-intensity problems, but reaffirm the irreplaceable value of human therapists in emotionally complex interactions.