Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuity
- URL: http://arxiv.org/abs/2512.06106v1
- Date: Fri, 05 Dec 2025 19:24:18 GMT
- Title: Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuity
- Authors: Constanze Albrecht, Chayapatr Archiwaranguprok, Rachel Poonsiriwong, Awu Chen, Peggy Yin, Monchai Lertsutthiwong, Kavin Winson, Hal Hershfield, Pattie Maes, Pat Pataranutaporn,
- Abstract summary: We report a randomized controlled study evaluating three modalities of AI-generated future selves (text, voice, avatar) against a neutral control condition.<n>All personalized modalities strengthened Future Self-Continuity (FSC), emotional well-being, and motivation compared to control, with avatar producing the largest vividness gains.<n> Interaction quality metrics, particularly persuasiveness, realism, and user engagement, emerged as robust predictors of psychological and affective outcomes.
- Score: 25.629127439391166
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: What if users could meet their future selves today? AI-generated future selves simulate meaningful encounters with a digital twin decades in the future. As AI systems advance, combining cloned voices, age-progressed facial rendering, and autobiographical narratives, a central question emerges: Does the modality of these future selves alter their psychological and affective impact? How might a text-based chatbot, a voice-only system, or a photorealistic avatar shape present-day decisions and our feeling of connection to the future? We report a randomized controlled study (N=92) evaluating three modalities of AI-generated future selves (text, voice, avatar) against a neutral control condition. We also report a systematic model evaluation between Claude 4 and three other Large Language Models (LLMs), assessing Claude 4 across psychological and interaction dimensions and establishing conversational AI quality as a critical determinant of intervention effectiveness. All personalized modalities strengthened Future Self-Continuity (FSC), emotional well-being, and motivation compared to control, with avatar producing the largest vividness gains, yet with no significant differences between formats. Interaction quality metrics, particularly persuasiveness, realism, and user engagement, emerged as robust predictors of psychological and affective outcomes, indicating that how compelling the interaction feels matters more than the form it takes. Content analysis found thematic patterns: text emphasized career planning, while voice and avatar facilitated personal reflection. Claude 4 outperformed ChatGPT 3.5, Llama 4, and Qwen 3 in enhancing psychological, affective, and FSC outcomes.
Related papers
- Lost Before Translation: Social Information Transmission and Survival in AI-AI Communication [7.593123083236325]
We study what happens when AI talks to AI.<n>In five studies tracking content through AI transmission chains, we find three consistent patterns.<n>We show that the properties that make AI-mediated content appear authoritative may systematically erode the cognitive and affective diversity on which informed judgment depends.
arXiv Detail & Related papers (2026-01-21T17:18:46Z) - AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations [26.039188521284974]
We show how AI personality traits can shape users' self-concepts through human-AI conversation.<n>We provide important design implications for developing more responsible and ethical AI systems.
arXiv Detail & Related papers (2026-01-19T05:16:57Z) - Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice [9.15392262037427]
We introduce AI-enabled digital twins that have lived through'' simulated life scenarios.<n>Rather than predicting optimal outcomes, these simulations extend prospective cognition by making alternative futures vivid.
arXiv Detail & Related papers (2025-12-05T03:30:44Z) - TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation [55.55404595177229]
Large Language Models (LLMs) are exhibiting emergent human-like abilities.<n>TwinVoice is a benchmark for assessing persona simulation across diverse real-world contexts.
arXiv Detail & Related papers (2025-10-29T14:00:42Z) - HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning [0.4931504898146351]
textbfHumAIne-chatbot is an AI-driven conversational agent that personalizes responses through a novel user profiling framework.<n>During live interactions, an online reinforcement learning agent refines per-user models by combining implicit signals.<n>Results show consistent improvements in user satisfaction, personalization accuracy, and task achievement when personalization features were enabled.
arXiv Detail & Related papers (2025-09-04T15:16:38Z) - Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models [75.85319609088354]
Sentient Agent as a Judge (SAGE) is an evaluation framework for large language models.<n>SAGE instantiates a Sentient Agent that simulates human-like emotional changes and inner thoughts during interaction.<n>SAGE provides a principled, scalable and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents.
arXiv Detail & Related papers (2025-05-01T19:06:10Z) - Future You: A Conversation with an AI-Generated Future Self Reduces Anxiety, Negative Emotions, and Increases Future Self-Continuity [20.404091166696052]
We introduce "Future You," an interactive, brief, single-session, digital chat intervention designed to improve future self-continuity.
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.
After a brief interaction with the "Future You" character, users reported decreased anxiety, and increased future self-continuity.
arXiv Detail & Related papers (2024-05-21T06:00:51Z) - Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system.
We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context.
Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
arXiv Detail & Related papers (2024-04-03T08:48:50Z) - Digital Life Project: Autonomous 3D Characters with Social Intelligence [86.2845109451914]
Digital Life Project is a framework utilizing language as the universal medium to build autonomous 3D characters.
Our framework comprises two primary components: SocioMind and MoMat-MoGen.
arXiv Detail & Related papers (2023-12-07T18:58:59Z) - CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset
for Conversational AI [48.67259855309959]
Most existing datasets for conversational AI ignore human personalities and emotions.
We propose CPED, a large-scale Chinese personalized and emotional dialogue dataset.
CPED contains more than 12K dialogues of 392 speakers from 40 TV shows.
arXiv Detail & Related papers (2022-05-29T17:45:12Z) - Evaluating and Inducing Personality in Pre-trained Language Models [78.19379997967191]
We draw inspiration from psychometric studies by leveraging human personality theory as a tool for studying machine behaviors.
To answer these questions, we introduce the Machine Personality Inventory (MPI) tool for studying machine behaviors.
MPI follows standardized personality tests, built upon the Big Five Personality Factors (Big Five) theory and personality assessment inventories.
We devise a Personality Prompting (P2) method to induce LLMs with specific personalities in a controllable way.
arXiv Detail & Related papers (2022-05-20T07:32:57Z) - EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments [75.11753644302385]
Empathetic conversational agents should not only understand what is being discussed, but also acknowledge the implied feelings of the conversation partner.
We propose a method based on a transformer pretrained language model (T5)
We evaluate our model on the EmpatheticDialogues dataset using both automated metrics and human evaluation.
arXiv Detail & Related papers (2021-10-30T19:04:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.