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

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Authors: Xinyue Qi, Chengzhong Liu, Xiangyu LONG, Zhizhuo Kou, Sirui Han, Yike Guo
Cited by: 1
References: 101

Abstract

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.

Fields of Study & Subjects

Personal Information Management and User Behavior Innovative Human-Technology Interaction Career Development and Diversity

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–22
Publication date
13 April 2026
Language
EN