中国高职学生生成式人工智能使用需求现状：一项横断面调查

ORIGINAL RESEARCH article

Front. Psychol.

Sec. Educational Psychology

Abstract

Objective: This study aimed to investigate the current status of Generative Artificial Intelligence (GAI) usage needs among vocational college students from China, explore their relationships with learning engagement and academic resilience, and identify variables associated with GAI usage demand. Methods: Using convenience sampling, 3876 students from five vocational colleges in Sichuan Province were enrolled. Surveys were conducted using the Generative Artificial Intelligence Application Status and Usage Demand Scale, the Learning Engagement Scale, and the Academic Resilience Scale. Results: The average GAI needs score among vocational college students was 58.23 ± 12.77. Correlation analysis revealed positive relationships between GAI needs and both learning engagement and academic resilience (P < 0.01). Multiple linear regression analysis revealed that gender, academic year, availability of GAI learning devices at home, level of GAI understanding, enrollment in GAI-related courses, frequency of active GAI use, student-reported frequency of teachers' use of GAI in teaching, learning engagement, and academic resilience were independently associated with GAI usage demand (all P < 0.05). The model explained 24.4% of the This is a provisional file, not the final typeset article variance in GAI usage demand (R² = 0.244, adjusted R² = 0.241, F(14, 3861) = 88.932, P < 0.001). Conclusion: Vocational college students' GAI usage demand was slightly above the theoretical midpoint of the scale. Greater GAI usage demand was independently associated with male gender, earlier academic year, better availability of GAI learning devices at home, a higher level of GAI understanding, enrollment in GAI-related courses, more frequent active GAI use, more frequent student-reported teachers' use of GAI in teaching, higher learning engagement, and greater academic resilience. Because of the cross-sectional design, these findings should be interpreted as associations rather than causal relationships.

Keywords

Academic resilience, artificial intelligence, Generative artificial intelligence, Learning engagement, Vocational college students

Received

12 June 2026

Accepted

30 September 2026

Copyright

© 2026 Zeng, Zhang, Jiang, Gan, Feng, Xu, Huang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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