User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an intelligent agent that mimics the actions of a human user interacting with an AI system, enabling researchers to model and analyze user behaviour, generate synthetic data for training, and evaluate interactive AI systems in a controlled and reproducible manner. Because of its broad scope, research on this topic currently remains scattered across artificial intelligence, human-computer interaction, information science, computational social science, and psychology. To address this fragmented landscape of current research, this article presents a foundational synthesis. We highlight the paradigm shift from traditional predictive models to modern generative approaches, and explicitly frame critical ethical considerations -- demonstrating how controlled simulation serves not merely as a risk vector for bias, but as a powerful, proactive tool to ensure fair representation and system safety. Furthermore, we establish the theoretical connection between user simulation and the pursuit of Artificial General Intelligence, arguing that realistic simulators are indispensable catalysts for overcoming critical data and evaluation bottlenecks and optimizing personalization. Ultimately, we propose a practical, self-sustaining innovation ecosystem bridging academia and industry to advance this increasingly important technology.

翻译：用户模拟是生成式AI时代一个新兴的跨学科课题，具有多种关键应用。它涉及创建能够模拟人类用户与AI系统交互行为的智能代理，使研究人员能够以可控且可重现的方式建模和分析用户行为、生成用于训练的合成数据，以及评估交互式AI系统。由于其范围广泛，目前该课题的研究仍分散在人工智能、人机交互、信息科学、计算社会科学和心理学等多个领域。为应对当前研究领域的碎片化现状，本文提出了一项基础性综合研究。我们强调了从传统预测模型向现代生成式方法的范式转变，并明确阐述了关键的伦理考量——展示受控模拟不仅作为风险的偏见向量，更是一种确保公平表征和系统安全的前瞻性强大工具。此外，我们建立了用户模拟与追求通用人工智能之间的理论联系，论证了逼真的模拟器是克服关键数据与评估瓶颈、优化个性化不可或缺的催化剂。最后，我们提出了一个连接学术界与产业界的实用、自持续的创新生态体系，以推动这一日益重要的技术发展。