AI for Better UX in Computer-Aided Engineering: Is Academia Catching Up with Industry Demands? A Multivocal Literature Review

• URL: http://arxiv.org/abs/2507.16586v1
• Date: Tue, 22 Jul 2025 13:39:45 GMT
• Title: AI for Better UX in Computer-Aided Engineering: Is Academia Catching Up with Industry Demands? A Multivocal Literature Review
• Authors: Choro Ulan Uulu, Mikhail Kulyabin, Layan Etaiwi, Nuno Miguel Martins Pacheco, Jan Joosten, Kerstin Röse, Filippos Petridis, Jan Bosch, Helena Holmström Olsson,
• Abstract summary: Computer-Aided Engineering (CAE) enables simulation experts to optimize complex models, but faces challenges in user experience (UX) that limit efficiency and accessibility.<n>While artificial intelligence (AI) has demonstrated potential to enhance CAE processes, research integrating these fields with a focus on UX remains fragmented.<n>This paper presents a multivocal literature review (MLR) examining how AI enhances UX in CAE software across both academic research and industry implementations.
• Score: 3.5213888068272183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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