Exploring College Students' Engagement with Generative AI for Career Exploration

A new study conducted at Ankang University, in collaboration with other institutions, has shed light on the factors influencing Chinese college students' engagement with Generative AI (GenAI) for career exploration purposes. The research employed the Comprehensive Model of Information Seeking (CMIS) and the Technology Acceptance Model (TAM) to analyze the relationship between individual factors, such as work-relevant knowledge and experience, and technological readiness, and perceptions of the information carrier, including perceived usefulness and perceived output quality. The study gathered empirical data from 502 participants through a paper-based questionnaire and used a mixed-methods design, incorporating partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA).

Key Takeaways:

• The study found that college students' utilization of GenAI for career information seeking is influenced by both individual factors and perceptions of the information carrier.
• Four configurations influencing students' career information seeking behavior were identified through fsQCA: work-relevant knowledge & experience, technological readiness, and efficacy beliefs; perceived usefulness, perceived output quality, and perceived value.
• The findings highlight the complexity of information-seeking behaviors and underscore the necessity of balancing GenAI integration in career guidance education.
• The research provides theoretical support for CMIS as a viable framework beyond health information seeking and identifies practical applications and opportunities for future research on career information seeking.
• The study concludes that future studies should explore how to combine GenAI with other educational strategies to further enhance career preparation and address the limitations of GenAI in educational settings.
• The study involved 502 participants from Ankang University and was supported by the Shaanxi Provincial Department of Education.
• The research was conducted by Kang Wang, Yu-Yuan Qu, and Siew-Ping Wong, with additional support from Ankang University's Informat Construct & Management Center.

Statistics:

• 502 participants were involved in the study.
• The study used a mixed-methods design, incorporating PLS-SEM and fsQCA.
• Four configurations influencing students' career information seeking behavior were identified.
• The study found that individual factors, such as work-relevant knowledge and experience, and technological readiness, contributed to students' utilization of GenAI for career information seeking.
• Perceived usefulness, perceived output quality, and perceived value were also found to be influential in students' decision to use GenAI.

Sources:

• "Exploring College Students' Utilization of Generative AI for Career Information Seeking: an Integrated Model With Pls-sem and Fsqca Approach." Education and Information Technologies, 2025.
• "Recent Findings from Ankang University Has Provided New Information about Education and Information Technology (Exploring College Students' Utilization of Generative Ai for Career Information Seeking: an Integrated Model With Pls-sem and Fsqca ...)." Information Technology Newsweekly, May 20, 2025; p 568.