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

2025-07-22

AI Summary

CAE software suffers from persistent user experience (UX) bottlenecks that hinder industrial efficiency and accessibility; while AI holds significant promise for workflow optimization, AI-driven UX enhancement in CAE remains fragmented and lags behind industry needs. This paper pioneers a multi-voice literature review (MLR) to systematically synthesize academic publications and industrial case studies from the past decade, focusing on four AI technologies—large language models (LLMs), adaptive interfaces, recommendation systems, and workflow automation—and their implementation gaps in CAE UX. The analysis uncovers structural misalignments between academia and industry regarding objective formulation, evaluation criteria, and deployment pathways. Based on these findings, we propose a human-centered, AI-augmented UX framework comprising three dimensions: intelligent guidance, dynamic adaptation, and workflow autonomy. This framework provides both a theoretical mapping and actionable translational pathways for future research and industrial adoption.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• AI enhances UX in CAE software across academia and industry
• Gaps exist between academic research and industry AI implementations
• AI-powered guidance and adaptive interfaces need more exploration

Innovation

• AI-powered guidance for CAE UX enhancement
• Adaptive interfaces to improve user experience
• Workflow automation in CAE software