用AI导师个性化训练谈判领导力，实测效果优于传统方法
SocialCoach: Personalized Social Skill Learning with Agentic Tutoring and Practice
- 基于学习者画像和练习记录，动态生成定制化训练路径
- 在冷启动场景下，路径质量评分显著高于基线模型
- 适合希望提升社交能力的职场人士或学生群体
谈判与领导力等社交技能对个人和职业发展至关重要，但因专家辅导资源稀缺，规模化有效训练仍具挑战。本文提出SocialCoach，一个基于大模型的智能导师系统，用于个性化社交技能学习。该系统构建可追溯的策略、案例与实践场景语料库，用于调度与反思式教学。将个性化练习调度建模为冷启动、检索约束的序列调度问题，结合学习者画像、模拟能力状态与历史表现，通过策略生成结构化训练建议，并利用语料库检索实现。为提升调度效果，采用基于评分标准的轨迹级GRPO优化完整路径。系统已部署于实际平台，支持目标驱动练习与知识引导的反思教学。在合成冷启动设置中，实验表明SocialCoach在路径质量评分上优于基线；人类研究进一步验证其在真实社交技能学习中的有效性。
原文摘要 · Abstract (English)
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.
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