Exploring User Acceptance and Concerns toward LLM-powered Conversational Agents in Immersive Extended Reality

• URL: http://arxiv.org/abs/2512.15343v1
• Date: Wed, 17 Dec 2025 11:41:25 GMT
• Title: Exploring User Acceptance and Concerns toward LLM-powered Conversational Agents in Immersive Extended Reality
• Authors: Efe Bozkir, Enkelejda Kasneci,
• Abstract summary: The extended reality (XR) community has sought to integrate large language models (LLMs) to enhance user experience and task efficiency.<n>While users generally accept these technologies, they express concerns related to security, privacy, social implications, and trust.<n>Our results suggest that familiarity plays a crucial role, as daily generative AI use is associated with greater acceptance.
• Score: 16.53846784748676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of generative artificial intelligence (AI) and large language models (LLMs), and the availability of services that make them accessible, have led the general public to begin incorporating them into everyday life. The extended reality (XR) community has also sought to integrate LLMs, particularly in the form of conversational agents, to enhance user experience and task efficiency. When interacting with such conversational agents, users may easily disclose sensitive information due to the naturalistic flow of the conversations, and combining such conversational data with fine-grained sensor data may lead to novel privacy issues. To address these issues, a user-centric understanding of technology acceptance and concerns is essential. Therefore, to this end, we conducted a large-scale crowdsourcing study with 1036 participants, examining user decision-making processes regarding LLM-powered conversational agents in XR, across factors of XR setting type, speech interaction type, and data processing location. We found that while users generally accept these technologies, they express concerns related to security, privacy, social implications, and trust. Our results suggest that familiarity plays a crucial role, as daily generative AI use is associated with greater acceptance. In contrast, previous ownership of XR devices is linked to less acceptance, possibly due to existing familiarity with the settings. We also found that men report higher acceptance with fewer concerns than women. Regarding data type sensitivity, location data elicited the most significant concern, while body temperature and virtual object states were considered least sensitive. Overall, our study highlights the importance of practitioners effectively communicating their measures to users, who may remain distrustful. We conclude with implications and recommendations for LLM-powered XR.

Related papers

• Teaching Language Models To Gather Information Proactively [53.85419549904644]
  Large language models (LLMs) are increasingly expected to function as collaborative partners.<n>In this work, we introduce a new task paradigm: proactive information gathering.<n>We design a scalable framework that generates partially specified, real-world tasks, masking key information.<n>Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information.
  arXiv  Detail & Related papers  (2025-07-28T23:50:09Z)
• Evaluating Social Acceptance of eXtended Reality (XR) Agent Technology: A User Study (Extended Version) [37.69303106863453]
  We present the findings of a user study that evaluated the social acceptance of eXtended Reality (XR) agent technology.<n>This system involves user interaction with a virtual avatar, enabled by a modular toolkit.
  arXiv  Detail & Related papers  (2025-07-22T13:14:05Z)
• PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language Models [10.050972891318324]
  We propose a privacy preservation pipeline for protecting privacy and sensitive information during interactions between users and large language models.<n>We construct SensitiveQA, the first privacy open-ended question-answering dataset.<n>Our proposed solution employs a multi-stage strategy aimed at preemptively securing user information while simultaneously preserving the response quality of cloud-based LLMs.
  arXiv  Detail & Related papers  (2025-02-19T09:17:07Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Evaluating Cultural and Social Awareness of LLM Web Agents [113.49968423990616]
  We introduce CASA, a benchmark designed to assess large language models' sensitivity to cultural and social norms.<n>Our approach evaluates LLM agents' ability to detect and appropriately respond to norm-violating user queries and observations.<n>Experiments show that current LLMs perform significantly better in non-agent environments.
  arXiv  Detail & Related papers  (2024-10-30T17:35:44Z)
• Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation [70.52558242336988]
  We focus on predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a multimodal transcript''
  arXiv  Detail & Related papers  (2024-09-13T18:28:12Z)
• Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacy [37.061999275101904]
  We argue for using large language models in XR by embedding them in avatars or as narratives to facilitate inclusion. We speculate that combining the information provided to LLM-powered spaces by users and the biometric data obtained might lead to novel privacy invasions.
  arXiv  Detail & Related papers  (2024-02-06T11:19:40Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Countering Misinformation via Emotional Response Generation [15.383062216223971]
  proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and democracy. Previous research has shown how social correction can be an effective way to curb misinformation. We present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs.
  arXiv  Detail & Related papers  (2023-11-17T15:37:18Z)
• "It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents [27.480959048351973]
  The widespread use of Large Language Model (LLM)-based conversational agents (CAs) raises many privacy concerns. We analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs.
  arXiv  Detail & Related papers  (2023-09-20T21:34:36Z)
• SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration [75.62448812759968]
  This dataset is a large-scale Korean dataset of 49k sensitive questions with 42k acceptable and 46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA in a human-in-the-loop manner based on real news headlines.
  arXiv  Detail & Related papers  (2023-05-28T11:51:20Z)
• SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning [61.335252950832256]
  Sense-Plan-Ask, or SPA, generates plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication.
  arXiv  Detail & Related papers  (2020-02-08T23:15:06Z)

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.