How would prompt completion editing impact user experience scores in academic research with large language models?

Prompt completion editing (PCE), the user-driven revision of large language model (LLM) completions, is a critical behaviour in the academic applications of multimodal LLMs. However, few studies have examined how these edits function as implicit reinforcement signals to improve LLM alignment and enhance user experience (UX). This study investigates how PCE, conceptualised through the dimensions of language stylistics, personalisation, and labelling, affects UX outcomes, including performance, task management, and user satisfaction. The sample consisted of 294 respondents from China and Kenya. Using a user-centred approach, this study applies partial least squares structural equation modelling for empirical analysis. The results show that PCE significantly improves UX (β = 0.304, t = 3.965, p < 0.001) and acts as a proxy for implicit human feedback in LLM optimisation. Mediation analysis confirms that data management and prompting experience significantly explain the relationships (p < 0.001), whereas simple slope analysis supports the moderation effects of perceived usefulness, task fit, and quality. The findings suggest that user edits serve as fine-grained feedback signals that enhance personalisation and usability in academic contexts. These results inform the design of more flexible and feedback-aware LLM systems, thereby advancing the development of human-in-the-loop artificial intelligence.