SocialCoach: 基于强化学习的智能辅导与练习的个性化社交技能学习
SocialCoach: Personalized Social Skill Learning with Agentic Tutoring and Practice
AI总结：
提出SocialCoach系统，利用多智能体管道构建知识语料库、强化学习优化自适应练习调度，并结合沉浸式实践与反思辅导，以解决社交技能学习中专家辅导稀缺和知行差距问题。
摘要：
社交技能如谈判和领导力在当今互联世界中对于个人和职业成功至关重要。然而，由于专家辅导的稀缺，可扩展且有效的培训仍然是一个重大挑战。在本文中，我们介绍了SocialCoach，一个全面的LLM驱动的智能辅导系统，用于大规模个性化社交技能发展。首先，SocialCoach利用多智能体管道，从多样化的专家来源自动构建一个基于教学法的理论到实践知识语料库。其次，为了个性化学习旅程，它采用了一个自适应练习调度模块，遵循处方-检索-适应过程。为了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习者模拟环境中进行优化。最后，SocialCoach整合了沉浸式目标驱动练习、因果驱动能力评估和基于知识的反思辅导，以帮助解决知行差距。我们在产品EQoach中部署了该系统，并进行了广泛实验。结果表明，SocialCoach在模拟路径质量和评委评估的辅导质量上优于基线方法，而早期用户反馈表明其具有强烈的感知参与度和有用性。这些发现为个性化、游戏化的软技能学习教学平台提供了一种实用架构。
英文摘要：
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 work, we introduce SocialCoach, an LLM-powered agentic tutoring system for personalized social skill learning. SocialCoach constructs a theory-to-practice corpus of traceable strategies, cases, and practice scenarios, and uses this corpus for both scheduling and reflective tutoring. We formulate social practice personalization as cold-start, retrieval-constrained sequential practice scheduling. Given a learner profile, simulated proficiency state, and observed practice history, a policy produces structured prescriptions that are realized through corpus retrieval. To enhance scheduling effectiveness, we optimize complete pathways with trajectory-level GRPO using rubric-judge based pairwise preferences. Additionally, we instantiate the scheduling approach in a deployed platform with goal-driven practice and knowledge-grounded reflective tutoring. Finally, in the synthetic cold-start setting, experiment results show that SocialCoach achieves higher pathway-quality ratings than baselines in scheduling and tutoring quality. We also conduct human studies to demonstrate its usefulness for real-world social skill learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
