Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support

• URL: http://arxiv.org/abs/2506.16473v1
• Date: Thu, 19 Jun 2025 17:20:30 GMT
• Title: Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support
• Authors: Sophie Chiang, Guy Laban, Hatice Gunes,
• Abstract summary: This study investigates whether concerns shared with a robot align with those shared in human-to-human (H2H) therapy sessions.<n>We analyzed two datasets: one of interactions between users and professional therapists, and another involving supportive conversations with a social robot.<n>Results showed that 90.88% of robot conversation disclosures could be mapped to clusters from the human therapy dataset.
• Score: 6.987852837732702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As conversational agents increasingly engage in emotionally supportive dialogue, it is important to understand how closely their interactions resemble those in traditional therapy settings. This study investigates whether the concerns shared with a robot align with those shared in human-to-human (H2H) therapy sessions, and whether robot responses semantically mirror those of human therapists. We analyzed two datasets: one of interactions between users and professional therapists (Hugging Face's NLP Mental Health Conversations), and another involving supportive conversations with a social robot (QTrobot from LuxAI) powered by a large language model (LLM, GPT-3.5). Using sentence embeddings and K-means clustering, we assessed cross-agent thematic alignment by applying a distance-based cluster-fitting method that evaluates whether responses from one agent type map to clusters derived from the other, and validated it using Euclidean distances. Results showed that 90.88% of robot conversation disclosures could be mapped to clusters from the human therapy dataset, suggesting shared topical structure. For matched clusters, we compared the subjects as well as therapist and robot responses using Transformer, Word2Vec, and BERT embeddings, revealing strong semantic overlap in subjects' disclosures in both datasets, as well as in the responses given to similar human disclosure themes across agent types (robot vs. human therapist). These findings highlight both the parallels and boundaries of robot-led support conversations and their potential for augmenting mental health interventions.

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