Human-Centered Metrics for Evaluating Usability in Artificial Intelligence Generated Human Object Interactions

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Usability is key to assessing whether interactions reflect real-world engagement with designed artifacts. However, evaluating usability requires design and prototyping, which incur substantial time and financial costs. With recent advances in multimodal generative AI (GenAI), it has become possible to predict, to some extent, the usability that a given design will afford, offering a potential solution to this problem. However, existing approaches focus primarily on visual fidelity and lack systematic evaluation of interaction interpretability from a human-centered perspective. To address this gap, the present study introduces the concept of interaction GenAI Affordance Quotient (AI-AQ), a human-centered framework for evaluating human-object interaction(HOI) quality. AI-AQ decomposes interactions into three components: subject consistency, affordance quality, and common sense alignment, enabling structured assessment. We compare GenAI-generated HOIs with real HOIs across varying object familiarity through a two-stage user study (N=80 pilot; N=800 main), using object images and textual prompts in GPT-4o.