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.

翻译：职业探索具有不确定性，需要在信息有限且结果难以预测的情况下做出决策。虽然生成式人工智能为职业指导提供了新机遇，但现有系统大多依赖线性对话界面，其建议往往过于全面且理想化，忽视了现实职业发展轨迹的非线性与努力依赖特性。本研究提出CareerPooler系统，该生成式人工智能驱动系统采用台球桌隐喻，将职业发展模拟为空间叙事交互。用户击打代表里程碑、技能与随机事件的球体，其中的提示、碰撞与反弹机制体现了不确定性下的决策过程。通过对24名参与者的组内实验，与基准聊天机器人相比，CareerPooler在参与度、信息获取量、满意度及职业清晰度方面均呈现显著提升。定性分析表明，空间叙事交互能促进经验式学习，通过挫折培养韧性，并减轻心理负担。本研究为人工智能辅助职业探索系统的设计提供了新思路，更广泛地表明基于视觉的类比交互可使生成式系统更具吸引力与满意度。