EvAlignUX: Advancing UX Research through LLM-Supported Exploration of Evaluation Metrics
<|reference_start|>EvAlignUX: Advancing UX Research through LLM-Supported Exploration of Evaluation Metrics: Evaluating UX in the context of AI's complexity, unpredictability, and generative nature presents unique challenges. HCI scholars lack sufficient tool support to build knowledge around diverse evaluation metrics and develop comprehensive UX evaluation plans. In this paper, we introduce EvAlignUX, an innovative system grounded in scientific literature and powered by large language models (LLMs), designed to help HCI scholars explore evaluation metrics and their relationship to potential research outcomes. A user study involving 19 HCI scholars revealed that EvAlignUX significantly improved the perceived clarity, specificity, feasibility, and overall quality of their evaluation proposals. The use of EvAlignUX enhanced participants' thought processes, resulting in the creation of a Question Bank that can be used to guide UX Evaluation Development. Additionally, the influence of researchers' backgrounds on their perceived inspiration and concerns about over-reliance on AI highlights future research directions for AI's role in fostering critical thinking.<|reference_end|>
@article{zheng2024evalignux:,
    title={EvAlignUX: Advancing UX Research through LLM-Supported Exploration of
  Evaluation Metrics},
    author={Qingxiao Zheng, Minrui Chen, Pranav Sharma, Yiliu Tang, Mehul Oswal,
  Yiren Liu, Yun Huang},
    journal={arXiv preprint arXiv:2409.15471},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15471},
    primaryClass={cs.HC}
}