Remini: Leveraging Chatbot-Mediated Mutual Reminiscence for Promoting Positive Affect and Feeling of Connectedness among Loved Ones

2025-08-05

AI Summary

Existing reminiscence tools primarily emphasize individual reflection or unidirectional narration, neglecting the facilitative role of dynamic dialogue in co-constructing shared autobiographical memories. To address this gap, we propose a dialogic AI system grounded in the social function theory of autobiographical memory—the first to operationalize this theoretical framework in conversational agent design. We introduce a five-stage interaction protocol (trust establishment → narration → elaboration → reflection → synthesis) that supports bidirectional self-disclosure and scaffolds emotionally engaged, collaborative storytelling between close relational partners. Through structured conversational guidance, the system fosters reciprocal narrative co-construction. User studies demonstrate that, compared to baseline prompt-based approaches, our system significantly enhances positive affect, perceived relational connectedness, and user engagement, while eliciting richer, more balanced mutual disclosure and joint narrative generation.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Mutual reminiscence, defined as revisiting shared positive memories through reciprocal self-disclosure, strengthens emotional bonds, enhances well-being, and deepens intimacy. However, most technology-mediated reminiscence tools emphasize individual reflection or one-way storytelling, which overlooks the dynamic, interactive dialogue essential for meaningful mutual reminiscence. To address this limitation, we introduce Remini, a chatbot designed to support reciprocal self-disclosure between close partners such as couples, friends, or family members. Grounded in the Social Functions of Autobiographical Memory (SFAM) framework, Remini uses conversational AI to guide emotionally rich exchanges through five narrative phases: rapport building, memory narration, elaboration, reflection, and summary. In a mixed-method, both between- and within- subjects study (N = 48, 24 dyads), we compare Remini to a baseline chatbot that offers minimal memory-trigger prompts. Our findings show that structured guidance from Remini significantly improves positive affect, feeling of connection, and engagement. It also fosters more detailed narrative co-construction and greater reciprocal self-disclosure. Participant feedback highlights the practical value, perceived benefits, and design considerations of chatbot-mediated reminiscence. We contribute empirically grounded design implications for conversational agents that strengthen human connection through mutual reminiscence.

Problem

• Promoting positive affect through mutual reminiscence
• Enhancing feeling of connectedness among loved ones
• Addressing lack of interactive dialogue in reminiscence tools

Innovation

• Chatbot supports reciprocal self-disclosure between partners
• Uses AI for guided emotionally rich narrative phases
• Structured guidance improves affect, connection, engagement