发表机构
Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对年轻求职者在社交媒体职业探索时被动浏览的问题，开发JobMate交互式系统，将真实帖子转化为对话式AI代理，通过对比研究发现其能引导社会比较转向建设性自我重构并促进意义建构，还探讨了相关AI系统设计启示。
AI 中文摘要
年轻求职者常借助社交媒体与同龄人比较并理解职业可能性。然而，被动浏览动态信息存在矛盾：真实的同伴内容虽能提供情感基础，但也会引发有害的向上社会比较和认知过载。以往工作要么在不改变被动浏览方式的情况下对在线用户生成内容进行结构化以减少噪音，要么构建忽视真实人类体验的人工智能职业探索系统。为填补这一空白，我们开发了JobMate，一个将真实社交媒体职业帖子转化为基于角色的对话式人工智能代理的交互式系统，将互动从被动滚动转变为主动、个性化对话。我们进行了一项对比研究（N = 24，三个学科），将JobMate与原生RedNote浏览进行比较。研究表明，JobMate的人工智能介导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，同时通过积极的对话参与促进意义建构。然而，用户仍依赖真实同伴内容的真实性来获得情感基础。我们讨论了在社会比较背景下增强真实在线用户生成内容消费的人工智能系统的设计启示。
英文摘要
Young job seekers frequently explore their career possibilities by browsing peers' posts that share job-seeking experiences. However, static browsing requires them to reconstruct fragmented cases and privately judge what others' experiences mean for themselves, sometimes intensifying anxiety through upward social comparison. In this paper, we examine how transforming these posts into persona-grounded conversations reshapes this sensemaking process. We developed JobMate, a prototype featuring agents that have personas built upon peers' posts and follow the self-determination theory to converse with users. In a between-subjects comparative study with 24 participants, RedNote browsing exposed diverse trajectories but left reconstruction and comparison largely to users, whereas JobMate supported case selection and continued questioning. The conversations further prompted users to articulate previously implicit constraints and accept, challenge, or revise the agent's interpretations. We discuss design implications for combining authentic peer experiences with generative AI in career exploration.
Comments17 pages, 4 figures, 1 tables
