XR-CareerAssist: An Immersive Platform for Personalised Career Guidance Leveraging Extended Reality and Multimodal AI
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 8 Apr 2026.
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References · 8
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2020Beyond English-Centric Multilingual Machine Translationcited by 559
- 2020MLS: A Large-Scale Multilingual Dataset for Speech Researchcited by 467
- 2021A Survey on Neural Speech Synthesiscited by 182
- 2022A Survey of Vision-Language Pre-Trained Modelscited by 177
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