CareerCraft: Supporting New Graduates on Job Hunting with LLM-Assisted Self-Construction of Career Profile

要旨

Starting the job hunt is often challenging for new graduates, who face barriers in translating experiences into actionable career profiles due to limited self-awareness and unclear skill mapping. Through formative study with new graduates and early-career professionals, we concluded specific challenges in experience extraction, skill organization, and expressive confidence. Drawing on these insights, we designed CareerCraft, an interactive system that scaffolds the construction of coherent career stories and supports tailored job searching via experience card extraction, guided profile building, and LLM-powered recommendations. In a within-subject evaluation (N=16), participants rated the efficacy of CareerCraft against the baseline condition without the tool in improving profile structuring, clarifying their self-awareness and competencies, and supporting informed job direction choices. Based on the findings, we concluded that CareerCraft offered a promising pathway to career readiness among new graduates to the workforce. We further summarized the design considerations for LLM products emphasizing on users’ self-exploration.

著者
Xinyue Qi
THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY(GUANGZHOU), Guangzhou, Guangdong, China
Chengzhong Liu
Hong Kong Generative AI Research and Development Center, Hong Kong, China
Xiangyu Long
Hong Kong Generative AI Research and Development Center, Hong Kong, China
Zhizhuo Kou
HKUST, Hong Kong, Hong Kong
Sirui Han
The Hong Kong University of Science and Technology, Hong Kong, China
Yike Guo
The Hong Kong University of Science and Technology, Hongkong, China

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Learning in the AI Era

P1 - Room 131
7 件の発表
2026-04-17 18:00:00
2026-04-17 19:30:00