迈向在扩展现实中减少口试焦虑的具身对话智能体
Towards Embodied Conversational Agents for Reducing Oral Exam Anxiety in Extended Reality
AI总结：
本文探讨在扩展现实环境中结合具身对话智能体与大语言模型，为学生提供心理安全、自适应且可重复的口试演练，以缓解口试焦虑并提升表现。
摘要：
口试是高等教育中普遍但心理要求高的评估形式。许多学生经历强烈的焦虑，这可能损害认知表现并阻碍学业成功。本立场论文探讨了在扩展现实（XR）环境中使用具身对话智能体（ECAs）来支持准备口试的学生的潜力。我们提出了一个系统概念，将逼真的ECAs与支持实时运行的大语言模型（LLMs）相结合，以实现心理安全、自适应且可重复的口试场景演练。我们还讨论了这一设想系统的潜在益处与挑战。
英文摘要：
Oral examinations are a prevalent but psychologically demanding form of assessment in higher education. Many students experience intense anxiety, which can impair cognitive performance and hinder academic success. This position paper explores the potential of embodied conversational agents (ECAs) in extended reality (XR) environments to support students preparing for oral exams. We propose a system concept that integrates photorealistic ECAs with real-time capable large language models (LLMs) to enable psychologically safe, adaptive, and repeatable rehearsal of oral examination scenarios. We also discuss the potential benefits and challenges of such an envisioned system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
