Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Embodied conversational agents (ECAs) are increasingly more realistic and capable of dynamic conversations. In online surveys, anthropomorphic agents could help address issues like careless responding and satisficing, which originate from the lack of personal engagement and perceived accountability. However, there is a lack of understanding of how ECAs in user experience research may affect participant engagement, satisfaction, and the quality of responses. As a proof of concept, we propose an instrument that enables the incorporation of conversations with a virtual avatar into surveys, using on AI-driven video generation, speech recognition, and Large Language Models. In our between-subjects study, 80 participants (UK, stratified random sample of general population) either talked to a voice-based agent with an animated video avatar, or interacted with a chatbot. Across surveys based on two self-reported psychometric tests, 2,265 conversation responses were obtained. Statistical comparison of results indicates that embodied agents can contribute significantly to more informative, detailed responses, as well as higher yet more time-efficient engagement. Furthermore, qualitative analysis provides valuable insights for causes of no significant change to satisfaction, linked to personal preferences, turn-taking delays and Uncanny Valley reactions. These findings support the pursuit and development of new methods toward human-like agents for the transformation of online surveys into more natural interactions resembling in-person interviews.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 11 of its 98 references that are in the map. First posted on arXiv 4 Aug 2025.
Builds on
11 of 98 references.
References · 11
- 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 234
- 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 123
- 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55
- 2021A Review of Evaluation Practices of Gesture Generation in Embodied Conversational Agentscited by 24
- 2019Tell Me About Yourself: Using an AI-Powered Chatbot to Conduct Conversational Surveys with Open-ended Questionscited by 16
- 2021Image-to-Video Generation via 3D Facial Dynamicscited by 13
- 2024Artificial social influence via human-embodied AI agent interaction in immersive virtual reality (VR): Effects of similarity-matching during health conversationscited by 8
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- 2025A Robot-Led Intervention for Emotion Regulation: From Expression to Reappraisalcited by 5
- 2024LEXI: Large Language Models Experimentation Interfacecited by 3
- 2024Robotising Psychometrics: Validating Wellbeing Assessment Tools in Child-Robot Interactionscited by 1
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Citing papers per year: 2026: 1.
1 paper.
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- 2024The influence of persona and conversational task on social interactions with a LLM-controlled embodied conversational agent
- 2025To Embody or Not: The Effect Of Embodiment On User Perception Of LLM-based Conversational Agents
- 2026E3VA: Enhancing Emotional Expressiveness in Virtual Conversational Agents
- 2026Signals of AI Hallucination: Designing Hallucination-Aware Cues for Embodied Conversational Agents in VR
- 2023Designing a realistic peer-like embodied conversational agent for supporting children's storytelling
- 2024An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling
- 2025Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents