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.

CHI ’25, Yokohama, Japan

This paper addresses the common challenge of system performance degradation due to speech inconsistency and mismatched acoustic conditions across various domains in speaker verification tasks. We propose a Noise-Aware Quality Network designed to estimate a score based on speech quality and the presence of speech obscured by noise in real-world environments. The score, derived from the normalization of estimated speech quality evaluations, is incorporated into a proposed Noise-Aware Quality loss function, aiming to prioritize speech quality by weighting the embedding distances based on the quality score. Our methodology significantly improves speaker verification performance, particularly in noisy environments. Furthermore, our work highlights the importance of speech quality and the potential benefits of incorporating speech quality weight into the loss function for speaker verification tasks.

We introduce “Future You,” an interactive, brief, single-session, digital chat intervention designed to improve future self-continuity-the degree of connection an individual feels with a temporally distant future selfa characteristic that is positively related to mental health and wellbeing. Our system allows users to chat with a relatable yet AI-powered virtual version of their future selves that is tuned to their future goals and personal qualities. To make the conversation realistic, the system generates a “future memory”-a unique backstory for each user-that creates a throughline between the user's present age (between 18–30) and their life at age 60. The “Future You” character also adopts the persona of an age-progressed image of the user. In our preregistered study$(\mathrm{N}=344)$, we found that after a brief interaction with the “Future You” character, users reported significantly decreased anxiety and increased future self-continuity compared to control conditions. This is the first study successfully demonstrating the use of personalized AI-generated characters to improve users' future self-continuity and wellbeing.

This research full paper describes a web-based online learning platform that delivers financial literacy lessons via talking head videos of AI-generated personas with two additional core features: LLM-powered proactive chat-based question-and-answer interactivity, and personal choice of the AI instructor from a list of distinct personas. We conducted two comparative studies with a total of 233 Thai students aged 1825, which aim to 1) investigate the impact of interactivity and instructor selection on the learning experience, and 2) further explore the underlying factors at play with instructor selection by introducing AI instructors' backstories as an extra intervention. We found that enabling interactivity significantly enhanced learning motivation, perceived learning facilitation, engagement, and virtual instructors' humanness compared to the passive setting. Providing learners with a choice of AI instructors provided minimal additional benefit. However, the learner's feeling of relatedness toward the instructor is a significant positive predictor of learning motivation, positive emotion, and agent credibility, while goal alignment with the agent correlates with perceived learning facilitation, and admiration corresponds with perceived agent humanness. These findings underscore the potential of interactive virtual instructors-ones that interactively encourage learners to reflect on the teaching materials throughout the lesson through two-way interaction-in enhancing motivational and experiential aspects of remote education, even if they do not significantly impact comprehension, and the importance of promoting learner's relatedness and goal alignment with the agent in boosting other aspects of the learning experience.

We propose a joint optimization of Network Coding and MIMO techniques to improve the downlink channel throughput of a wireless base station. Specifically, we consider a MIMO base station with multiple transmit antennas that serves multiple users simultaneously by generating multiple signal beams with well-defined beamforming weight vectors where each beam intends for a particular user. Given a large number of users and a small number of transmit antennas, a base station must decide, at any transmission opportunity, which group of users it should transmit packets to, in order to maximize the overall throughput. To that end, we propose a method for grouping users that takes advantages of NC technique and the orthogonality of user channels to improve the overall throughput on both unicast and broadcast transmissions. Our simulation results indicate that the proposed method can efficiently increase the throughput over existing techniques, especially in highly lossy environments.

Limited bandwidth and high packet loss pose a serious challenge for video streaming over wireless networks. Even when packet loss in the medium is not present, the fluctuating available bandwidth due to varying number of active flows in a network causes problem for video streaming applications. In this paper, we propose to employ a novel admission control together with a rate-distortion optimized framework to maintain reasonable qualities for multiple concurrent video streams. In particular, we formulate an optimization problem to allocate the optimal transmission rates for each layered video streams jointly with the MAC protocol of a slightly modified 802.11x network. We show the hardness results of the optimization problem under various conditions. Furthermore, we show that a simple greedy layer-allocation algorithm is typically not optimal, although it can approximate the solution reasonably well under certain assumptions.