Artificial intelligence literacy, artificial intelligence anxiety, and career optimism among university music students: a cross-sectional study
- 1School of Music and Dance, Xihua University , ,
Abstract
Objective: To examine the levels of artificial intelligence (AI) literacy, AI anxiety, and career optimism among university music students, their associations, and the independent associations of AI literacy and AI anxiety with career optimism after adjustment for relevant background factors.
Methods: A cross-sectional survey was conducted from May to July 2026 among students from five undergraduate music institutions in eastern China using convenience sampling. A total of 453 valid questionnaires were included in the analysis. Participants completed the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS), the Chinese version of the Artificial Intelligence Anxiety Scale (AIAS), and the Career Optimism Subscale (COS). Data were analyzed using descriptive statistics, independent-samples t tests, one-way analysis of variance, Pearson correlation analysis, and hierarchical multiple linear regression.
Results: Mean scores for AI literacy, AI anxiety, and career optimism were 52.97 ± 10.05, 83.85 ± 22.96, and 37.87 ± 8.02, respectively. Students with prior AI training and those reporting more frequent AI use showed higher AI literacy and career optimism and lower AI anxiety. AI literacy was positively associated with career optimism (r = 0.444, p < 0.001), whereas AI anxiety was negatively associated with career optimism (r = −0.461, p < 0.001). After adjustment for gender, year of study, music specialization, prior AI training, and frequency of AI use, AI literacy remained positively associated with career optimism (β = 0.314, p < 0.001), whereas AI anxiety remained negatively associated with career optimism (β = −0.326, p < 0.001). The final model explained 33.0% of the variance in career optimism.
Conclusion: AI literacy and AI anxiety were independently associated with career optimism among university music students. These findings suggest that students' perceptions of AI-related competence and technological concerns are relevant to their career expectations. Music institutions may consider AI education that combines practical competence with critical understanding of AI's implications for artistic identity, professional value, and future career development. Future longitudinal research is needed to clarify the temporal relationships among AI literacy, AI anxiety, and career optimism.
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BibTeX
@article{du2026artificial,
title = {{Artificial intelligence literacy, artificial intelligence anxiety, and career optimism among university music students: a cross-sectional study}},
author = {Yang Du},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1961896},
url = {https://europepmc.org/article/PMC/PMC13719472},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence literacy, artificial intelligence anxiety, and career optimism among university music students: a cross-sectional study
AU - Yang Du
PY - 2026
DA - 2026/10/16
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1961896
UR - https://europepmc.org/article/PMC/PMC13719472
AB - Objective: To examine the levels of artificial intelligence (AI) literacy, AI anxiety, and career optimism among university music students, their associations, and the independent associations of AI literacy and AI anxiety with career optimism after adjustment for relevant background factors. Methods: A cross-sectional survey was conducted from May to July 2026 among students from five undergraduate music institutions in eastern China using convenience sampling. A total of 453 valid questionnaires were included in the analysis. Participants completed the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS), the Chinese version of the Artificial Intelligence Anxiety Scale (AIAS), and the Career Optimism Subscale (COS). Data were analyzed using descriptive statistics, independent-samples t tests, one-way analysis of variance, Pearson correlation analysis, and hierarchical multiple linear regression. Results: Mean scores for AI literacy, AI anxiety, and career optimism were 52.97 ± 10.05, 83.85 ± 22.96, and 37.87 ± 8.02, respectively. Students with prior AI training and those reporting more frequent AI use showed higher AI literacy and career optimism and lower AI anxiety. AI literacy was positively associated with career optimism (r = 0.444, p < 0.001), whereas AI anxiety was negatively associated with career optimism (r = −0.461, p < 0.001). After adjustment for gender, year of study, music specialization, prior AI training, and frequency of AI use, AI literacy remained positively associated with career optimism (β = 0.314, p < 0.001), whereas AI anxiety remained negatively associated with career optimism (β = −0.326, p < 0.001). The final model explained 33.0% of the variance in career optimism. Conclusion: AI literacy and AI anxiety were independently associated with career optimism among university music students. These findings suggest that students' perceptions of AI-related competence and technological concerns are relevant to their career expectations. Music institutions may consider AI education that combines practical competence with critical understanding of AI's implications for artistic identity, professional value, and future career development. Future longitudinal research is needed to clarify the temporal relationships among AI literacy, AI anxiety, and career optimism.
SN - 1664-1078
ER -