AI as a Bridge Across Ages: Exploring The Opportunities of Artificial Intelligence in Supporting Inter-Generational Communication in Virtual Reality

TL;DR

This work explored the opportunities of Artificial Intelligence to support inter-generational communication in VR and shows that AI-powered VR facilitates inter-generational communication by enhancing mutual understanding, fostering conversation fluency, and promoting active participation.

Abstract

Inter-generational communication is essential for bridging generational gaps and fostering mutual understanding. However, maintaining it is complex due to cultural, communicative, and geographical differences. Recent research indicated that while Virtual Reality (VR) creates a relaxed atmosphere and promotes companionship, it inadequately addresses the complexities of inter-generational dialogue, including variations in values and relational dynamics. To address this gap, we explored the opportunities of Artificial Intelligence (AI) in supporting inter-generational communication in VR. We developed three technology probes (e.g., Content Generator, Communication Facilitator, and Info Assistant) in VR and employed them in a probe-based participatory design study with twelve inter-generational pairs. Our results show that AI-powered VR facilitates inter-generational communication by enhancing mutual understanding, fostering conversation fluency, and promoting active participation. We also introduce several challenges when using AI-powered VR in supporting inter-generational communication and derive design implications for future VR platforms, aiming to improve inter-generational communication.

Figures (7)

• Figure 1: Illustrations of our AI probes. (1) Image generation: it generates images based on verbal descriptions provided by users. (2) 3D Object generation: it creates virtual 3D models according to users' verbal descriptions. (3) Scene generation: the virtual environment automatically switches based on the content of the conversation. (4) Chat facilitator: it intervenes verbally to balance conversations when one party dominates, and offers potential conversation topics through voice prompts during awkward silences. (5) Emotion visualization: analyzing the emotional tone of chat content, it displays bubble effects for positive emotions (left) and rain effects for negative emotions (right). (6) Info assistant: users press the button to input voice-based queries and receive spoken and text answers in response to their inquiries.
• Figure 2: The user study process is structured into distinct parts, each represented by a column. The session began with a background interview (a), followed by a comprehensive tutorial on VR operations and AI probe usage (b). Subsequently, participants engaged in an AI probe study, initially focusing on planning a day's itinerary (c), then shifting to sharing stories about their campus life (d). The session culminated in a co-design session, encompassing both brainstorming activities (e) and thorough in-depth interviews (f).
• Figure 3: Illustration of participants' suggestions for AI's visual representation: a) shows a realistic, context-appropriate AI appearance such as a tour guide describing the attraction. Participants indicated that such representations would be especially beneficial in VR-shared activities, enhancing immersion and providing convincing guidance; b) depicts an inanimate virtual agent as a robot. Participants believed that it reduces their concerns about privacy and authenticity, making them more suitable for private conversations; c) represents a friendly and cute pet as a virtual agent. Participants believed that such representations were effective in easing tension and adding humor to interactions.
• Figure 4: Illustrations of participant-proposed AI interaction strategies in VR. Two activation methods were envisioned on the left: a1) passive activation, which emphasizes user autonomy and aims to minimize interruptions; and a2) proactive activation, where the virtual environment in VR dynamically changes in response to the user's emotions or conversation content. Two intervention strategies are proposed on the right: b1) prompts visible to both parties involved in the communication; b2) private prompts designed to discreetly remind the user of their behavior.
• Figure 5: Pre-generated 3D objects