Autodesign? Large Language Model Evaluations for UX Assistants
Review and Guidelines
DOI:
https://doi.org/10.29147/datjournal.v11i2.1034Keywords:
LLM, Evaluation, Design, UX Research, UX WritingAbstract
Large language models (LLMs) are being rapidly integrated into design workflows for Design Systems, UX research, and UX writing, but systematic methods to evaluate their safety and utility are lacking. Based on a scoping review, this paper maps key evaluation challenges and proposes three frameworks specific to these tasks. Each framework connects design activities to measurable success criteria (e.g., ≤ 3% hallucination, < 2s latency) and suggests datasets for reproducible testing. A demonstration with GPT-4o and Gemini 2.5 illustrates how the metrics identify the models' strengths and weaknesses. The guidelines offer design teams a practical roadmap to incorporate continuous, evidence-based testing, promoting greater rigor and accountability in AI-assisted design practice.
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