Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Multimodal Study of Human-AI Interaction
As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship. We present a longitudinal multimodal study of a memory-augmented conversational agent (24 participants x 10 sessions), in which participants rated five relational constructs -- familiarity, self-disclosure, perceived memory, conversational quality, and enjoyment -- after each session. Two complementary dynamics emerge. First, conversational quality strongly shape
Record details
Published: 16 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Agents & autonomy · Transparency
Retrieved: 17 July 2026
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ethics.ai (16 July 2026), “Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Multimodal Study of Human-AI Interaction,” evidence record 11304, https://ethics.ai/record/11304 (originally published by arXiv cs.HC).
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