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

arXiv: https://arxiv.org/abs/2509.11461 · HTML: https://mlsanity.com/paper/2509.11461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 73 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- [Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce](https://mlsanity.com/paper/2506.06576/) (2025)
- [Patchview: LLM-Powered Worldbuilding with Generative Dust and Magnet Visualization](https://mlsanity.com/paper/2408.04112/) (2024)
- [Future You: A Conversation with an AI-Generated Future Self Reduces Anxiety, Negative Emotions, and Increases Future Self-Continuity](https://mlsanity.com/paper/2405.12514/) (2024)
- [Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration](https://mlsanity.com/paper/2502.18881/) (2025)
- [Toyteller: AI-powered Visual Storytelling Through Toy-Playing with Character Symbols](https://mlsanity.com/paper/2501.13284/) (2025)
- [Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPT](https://mlsanity.com/paper/2401.08405/) (2024)
- [Towards Intent-based User Interfaces: Charting the Design Space of Intent-AI Interactions Across Task Types](https://mlsanity.com/paper/2404.18196/) (2024)
- [Steve: LLM Powered ChatBot for Career Progression](https://mlsanity.com/paper/2504.03789/) (2025)
- [VizCV: AI-assisted visualization of researchers' publications tracks](https://mlsanity.com/paper/2505.08691/) (2025)
- [Virtual Interviewers, Real Results: Exploring AI-Driven Mock Technical Interviews on Student Readiness and Confidence](https://mlsanity.com/paper/2506.16542/) (2025)

## Built on by (most cited first, 1 of 1)

- [StreetDesignAI: Broadening Designer Perspectives Through Multi-Persona Evaluation of Cycling Infrastructure](https://mlsanity.com/paper/2601.15671/) (2026)

## Abstract (arXiv)

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.