CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration

2025-09-14

AI Summary

Career exploration is inherently uncertain and nonlinear, yet existing generative AI guidance systems predominantly rely on linear dialogues, delivering idealized, generic advice that overlooks the contingency and effort-dependence of real-world career pathways. To address this, we propose the “Billiards Metaphor” interaction framework: mapping career milestones, skills, and stochastic events onto physically simulated, colliding, and rebounding balls within a dynamic table-space, enabling embodied, narrative-driven path generation. This work introduces, for the first time, a visual analogy-based interaction mechanism grounded in physics metaphors to concretize unpredictability and path dependence in career development. Integrating generative AI with interactive visualization, the system supports action-triggered path simulation. A 24-participant user study demonstrates significant improvements in engagement, information acquisition efficiency, satisfaction, and career clarity. Qualitative analysis further confirms its efficacy in fostering experiential learning, resilience, and reduced psychological burden.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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, 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.

Problem

• Simulates career development with spatial-narrative interaction
• Addresses non-linear nature of real-world career trajectories
• Reduces psychological burden in uncertain career exploration

Innovation

• Pool-table metaphor for career simulation
• Spatial-narrative interaction with generative AI
• Visual analogical interactions reduce psychological burden