![[2509.11461] CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2509.11461] CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration
Summary
CareerPooler introduces an AI-driven metaphorical simulation for career exploration, enhancing user engagement and decision-making through spatial interactions.
Why It Matters
As career exploration becomes increasingly complex, innovative tools like CareerPooler leverage generative AI to provide more engaging and realistic guidance. This approach addresses the limitations of traditional chat interfaces, offering a more interactive and less burdensome way for users to navigate their career paths.
Key Takeaways
- CareerPooler uses a pool-table metaphor to simulate career development.
- The system significantly enhances user engagement and satisfaction compared to traditional chatbots.
- Spatial-narrative interactions promote experience-based learning and resilience.
- Qualitative results indicate reduced psychological burden during career exploration.
- The findings suggest broader applications for visually grounded interactions in AI systems.
Computer Science > Human-Computer Interaction arXiv:2509.11461 (cs) [Submitted on 14 Sep 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration Authors:Ziyi Wang, Ziwen Zeng, Yuan Li, Zijian Ding View a PDF of the paper titled CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration, by Ziyi Wang and 2 other authors View PDF HTML (experimental) Abstract: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, an...
