用真人职业动态打造对话型AI助手，帮年轻人更积极地探索职业路径。
Same Stories, Different Journeys: From Social Comparison to Sensemaking in AI-Mediated Peer Career Exploration
- 将真实社交平台职业内容转化为可对话的AI角色，变被动浏览为主动交流。
- 实验显示用户从负面攀比转向自我重构，且更专注职业意义理解。
- 适合关注AI辅助职业咨询与社交媒体心理影响的研究者与设计者。
年轻求职者常通过社交媒体对比同龄人并理解职业可能性。然而，被动浏览信息流带来悖论：真实同伴内容虽具情感支撑作用，却易引发有害的向上社会比较和认知过载。现有工作或仅结构化内容但保留被动模式，或构建AI系统却忽视真实人类体验。为此，我们开发了JobMate——一个将真实社交平台职业帖转化为人格化对话型AI代理的交互系统，将被动滚动转变为个性化主动对话。在跨组实验中（N=24，三个学科），我们对比了JobMate与原生RedNote浏览。结果表明，JobMate的AI对话将社会比较从潜在有害的向上比较转向建设性的自我重构，并通过主动对话促进意义建构。然而，用户仍依赖真实同伴内容获取情感支持。本文讨论了在社会比较情境下增强真实用户生成内容消费的AI系统设计启示。
原文摘要 · Abstract (English)
Young job seekers frequently turn to social media to compare themselves with peers and make sense of career possibilities. However, passive feed browsing creates a paradox: the authentic peer content that provides emotional grounding also triggers potentially detrimental upward social comparison and cognitive overload. Previous work has either structured online user-generated content to reduce noise without changing the passive browsing modality, or built AI-powered career exploration systems that disregard authentic human experiences. To address this gap, we developed JobMate, an interactive system that transforms real social media career posts into persona-grounded conversational AI agents, shifting the interaction from passive scrolling to active, personalized dialogue. We conducted a between-subjects study ($N$ = 24, three disciplines) comparing JobMate with native RedNote browsing. Our study shows that JobMate's AI-mediated dialogue redirected social comparison from potentially detrimental upward comparison toward constructive self-reframing, while promoting sensemaking through active conversational engagement. However, users still relied on the authenticity of real peer content for emotional grounding. We discuss design implications for AI systems that augment authentic online user-generated content consumption across social comparison contexts.
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