Social skills such as negotiation and leadership are crucial for personal and professional success in today's interconnected world. However, scalable and effective training remains a significant challenge due to the scarcity of expert coaching. In this paper, we introduce SocialCoach, a holistic LLM-powered agentic tutoring system for personalized social skill development at scale. First, SocialCoach automatically constructs a pedagogically-grounded, theory-to-practice knowledge corpus from diverse expert sources, leveraging a multi-agent pipeline. Second, to personalize the learning journey, it employs an adaptive practice scheduling module that follows a prescription-retrieval-adaptation process. To maximize the long-term learning experience while overcoming the cold-start problem, this policy is optimized within a learner simulation environment through reinforcement learning. Finally, SocialCoach integrates immersive, goal-driven practice, causality-driven proficiency assessment and knowledge-grounded, reflective tutoring to help address the knowing-doing gap. We deploy it in our product, EQoach, and conduct extensive experiments. The results show that SocialCoach improves simulated pathway quality and judge-rated tutoring quality over baseline approaches, while early user feedback indicates strong perceived engagement and usefulness. These findings suggest a practical architecture for personalized and gamified pedagogical platforms on soft skill learning.

翻译：社交技能（如谈判与领导力）在当今互联世界中，对个人及职业成功至关重要。然而，由于专家辅导的稀缺性，规模化且有效的培训仍面临重大挑战。本文提出SocialCoach，一种基于大语言模型（LLM）驱动的全链路代理辅导系统，旨在实现大规模个性化社交技能发展。首先，SocialCoach利用多智能体管线，从多样化专家资源中自动构建具有教学理论基础、涵盖理论到实践的知识语料库。其次，为个性化学习路径，系统采用遵循“处方-检索-适配”流程的自适应练习调度模块。为在克服冷启动问题的同时最大化长期学习体验，该策略通过强化学习在学员模拟环境中进行优化。最后，SocialCoach整合沉浸式目标驱动练习、因果驱动能力评估及基于知识的反思性辅导，以帮助解决“知行差距”问题。我们在产品EQoach中部署该系统并开展广泛实验。结果表明，与基线方法相比，SocialCoach提升了模拟路径质量与评委评定的辅导质量，而早期用户反馈则显示出高感知参与度与实用性。这些发现为软技能学习的个性化与游戏化教学平台提供了实用架构。