Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuity

Connecting with one’s future self has been shown to enhance decision-making, improve academic performance, promote positive health outcomes, and elevate subjective quality of life. Yet traditional interventions rely on imagination or static visualizations that may not be the most effective. AI-generated digital twins offer a new approach, enabling people to engage in dialogue with a personalized representation of themselves decades ahead. However, it remains unclear how presentation modality shapes their psychological impact. We report a randomized between-subjects study (n = 92) comparing three modalities of an AI-generated future self (text, voice, and a photorealistic talking avatar) against a generic AI control. Our system integrated age progression, voice cloning, and facial animation to create personalized digital twins. All personalized modalities significantly strengthened participants’ connection to their future selves, particularly in how vividly and positively they could imagine who they will become. Although the avatar produced the largest gain in vividness, effects were comparable across modalities. Instead, subjective interaction quality, especially perceived persuasiveness, realism, and engagement, strongly predicted gains in future self-continuity and affect, indicating that experiential quality matters more than interface form. Conversation analysis revealed modality-specific patterns, with text emphasizing instrumental career planning and voice-based interactions eliciting more existential reflection. These findings indicate that effective future-self interventions do not necessarily rely on resource-intensive architecture and can scale through less demanding formats. At the same time, they raise ethical considerations about the implications of persuasive AI that engages users’ own identities.