Conventional career guidance platforms rely on static, text-driven interfaces that struggle to engage users or deliver personalised, evidence-based insights. Although Computer-Assisted Career Guidance Systems have evolved since the 1960s, they remain limited in interactivity and pay little attention to the narrative dimensions of career development. We introduce XR-CareerAssist, a platform that unifies Extended Reality (XR) with several Artificial Intelligence (AI) modules to deliver immersive, multilingual career guidance. The system integrates Automatic Speech Recognition for voice-driven interaction, Neural Machine Translation across English, Greek, French, and Italian, a Langchain-based conversational Training Assistant for personalised dialogue, a BLIP-based Vision-Language model for career visualisations, and AWS Polly Text-to-Speech delivered through an interactive 3D avatar. Career trajectories are rendered as dynamic Sankey diagrams derived from a repository of more than 100,000 anonymised professional profiles. The application was built in Unity for Meta Quest 3, with backend services hosted on AWS. A pilot evaluation at the University of Exeter with 23 participants returned 95.6% speech recognition accuracy, 78.3% overall user satisfaction, and 91.3% favourable ratings for system responsiveness, with feedback informing subsequent improvements to motion comfort, audio clarity, and text legibility. XR-CareerAssist demonstrates how the fusion of XR and AI can produce more engaging, accessible, and effective career development tools, with the integration of five AI modules within a single immersive environment yielding a multimodal interaction experience that distinguishes it from existing career guidance platforms.

翻译：传统职业指导平台依赖静态的文本驱动界面，难以吸引用户或提供个性化、基于实证的见解。尽管自20世纪60年代以来，计算机辅助职业指导系统不断发展，但其交互性仍然有限，且较少关注职业发展的叙事维度。我们提出XR-CareerAssist平台，该平台将扩展现实（XR）与多个人工智能（AI）模块相结合，提供沉浸式、多语种的职业指导。该系统集成了用于语音交互的自动语音识别、覆盖英语、希腊语、法语和意大利语的神经机器翻译、基于Langchain的对话式培训助手（用于个性化对话）、基于BLIP的视觉语言模型（用于职业可视化），以及通过交互式3D虚拟形象呈现的AWS Polly文本转语音功能。职业发展轨迹以动态桑基图形式呈现，数据源自超过10万个匿名专业档案库。该应用基于Unity构建，运行于Meta Quest 3平台，后端服务托管于AWS。在埃克塞特大学对23名参与者进行的试点评估显示，语音识别准确率达95.6%，整体用户满意度为78.3%，系统响应性好评率为91.3%，反馈意见为后续改进运动舒适度、音频清晰度和文本可读性提供了依据。XR-CareerAssist展示了XR与AI融合如何能够打造更具吸引力、更易访问且更有效的职业发展工具：在单一沉浸式环境中集成五个AI模块，创造出区别于现有职业指导平台的多模态交互体验。