AI提升工程软件用户体验，学术研究与工业应用存在明显差距。
AI for Better UX in Computer-Aided Engineering: Is Academia Catching Up with Industry Demands? A Multivocal Literature Review
- 通过多视角文献综述，分析AI如何改善CAE软件的用户交互体验。
- 企业已广泛应用大模型、自适应界面和推荐系统，学术研究却缺乏用户体验验证。
- 指出引导式AI、自适应界面和流程自动化是未来研究重点，适合人机交互与工业软件开发者参考。
计算机辅助工程（CAE）使仿真专家能够优化复杂模型，但其用户体验（UX）问题限制了效率与可及性。尽管人工智能（AI）在提升CAE流程方面展现出潜力，但聚焦于用户体验的跨领域研究仍呈碎片化状态。本文开展多视角文献综述（MLR），考察AI如何在学术研究与工业实践中增强CAE软件的用户体验。分析发现，学术探索与产业应用之间存在显著差距：企业已在实际中部署大型语言模型（LLMs）、自适应用户界面（UI）和推荐系统，而学术研究主要关注技术能力，缺乏对用户体验的实证验证。关键发现表明，基于AI的引导机制、自适应界面及工作流自动化等方向仍处于研究空白。通过绘制两领域的交集，本研究为未来弥补研究缺口、推动AI更好地改善CAE用户体验提供基础支持。
原文摘要 · Abstract (English)
Computer-Aided Engineering (CAE) enables simulation experts to optimize complex models, but faces challenges in user experience (UX) that limit efficiency and accessibility. While artificial intelligence (AI) has demonstrated potential to enhance CAE processes, research integrating these fields with a focus on UX remains fragmented. This paper presents a multivocal literature review (MLR) examining how AI enhances UX in CAE software across both academic research and industry implementations. Our analysis reveals significant gaps between academic explorations and industry applications, with companies actively implementing LLMs, adaptive UIs, and recommender systems while academic research focuses primarily on technical capabilities without UX validation. Key findings demonstrate opportunities in AI-powered guidance, adaptive interfaces, and workflow automation that remain underexplored in current research. By mapping the intersection of these domains, this study provides a foundation for future work to address the identified research gaps and advance the integration of AI to improve CAE user experience.
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