Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
August 04, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Matus Krajcovic, Peter Demcak, Eduard Kuric
arXiv ID
2508.02376
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted