用台球模拟职业发展，让探索更直观有趣。
CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration
- 用台球桌比喻职业路径，击球代表关键决策。
- 24人实验显示参与度、信息获取和清晰度显著提升。
- 适合想体验式学习职业规划的人群。
职业探索充满不确定性，常需在信息不足、结果不可预测的情况下做决定。尽管生成式AI为职业指导带来新可能，但多数系统仍依赖线性聊天界面，输出内容过于全面且理想化，忽略了现实中职业发展的非线性和付出努力的特性。我们提出CareerPooler，一个基于生成式AI的系统，采用台球桌隐喻来模拟职业发展过程，将里程碑、技能与随机事件表现为球体，通过提示、碰撞与反弹体现决策中的不确定性。在包含24名参与者的组内研究中，相比聊天机器人基线，CareerPooler显著提升了用户参与度、信息获取量、满意度及职业清晰度。定性分析表明，空间叙事交互有助于经验式学习、增强应对挫折的韧性，并减轻心理负担。研究为智能职业探索系统设计提供新思路，也表明视觉化类比交互可使生成式系统更具吸引力与满足感。
原文摘要 · Abstract (English)
Career exploration is uncertain, requiring decisions with limited information and unpredictable outcomes. While generative AI offers new opportunities for career guidance, most systems rely on linear chat interfaces that produce overly comprehensive and idealized suggestions, overlooking the non-linear and effortful nature of real-world trajectories. We present CareerPooler, a generative AI-powered system that employs a pool-table metaphor to simulate career development as a spatial and narrative interaction. Users strike balls representing milestones, skills, and random events, where hints, collisions, and rebounds embody decision-making under uncertainty. In a within-subjects study with 24 participants, CareerPooler significantly improved engagement, information gain, satisfaction, and career clarity compared to a chatbot baseline. Qualitative findings show that spatial-narrative interaction fosters experience-based learning, resilience through setbacks, and reduced psychological burden. Our findings contribute to the design of AI-assisted career exploration systems and more broadly suggest that visually grounded analogical interactions can make generative systems engaging and satisfying.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。