ER
Generative AI for University Students’ Career Planning: An Educational Pathway
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Abstract
Generative artificial intelligence (AI) can produce detailed career advice, yet a persuasive plan offers limited educational value when students cannot explain its assumptions or test it through experience. The pathway developed here connects career exploration, learning guidance, and labor education in Chinese higher education. It draws on research concerning AI career support and self-regulated learning, together with international educational guidance and China’s labor education framework. The pathway begins with students identifying a career question from their own experience. They then examine AI suggestions against verified occupational information, translate an unresolved question into a learning task, and complete practical work that produces evidence for revising their plans. Teachers intervene when information, student expectations, and practical feedback conflict. Assessment centers on the reasons for a choice, the quality of work evidence, and the justification for subsequent revision. An illustrative community service task shows how online guidance can inform practical activity and how experience can redirect further inquiry. The article contributes an instructional arrangement in which career planning develops through student-produced evidence. Its educational value requires evaluation through implementation, particularly regarding access to meaningful practice and the quality of student judgment.
Keywords
Career exploration; self-regulated learning; labor education; work experience; student agency
References
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How to cite this paper
Generative AI for University Students’ Career Planning: An Educational Pathway
How to cite this paper: Guohui Liu, Xianpan Xie. (2026). Generative AI for University Students’ Career Planning: An Educational Pathway. The Educational Review, USA, 10(8), 431-435.
DOI: http://dx.doi.org/10.26855/er.2026.08.002
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