AI Education Exposure and Its Effects on Innovation Capability and Career Adaptability Among Chinese Undergraduate Students
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Abstract
Artificial intelligence (AI) is increasingly embedded in higher education, yet empirical evidence remains limited regarding whether students’ AI-Supported Learning Experience (ASLE) is associated with broader developmental outcomes such as innovation ability and career adaptability within a single explanatory model. This study employed a quantitative cross-sectional survey design involving 411 undergraduate students from four universities in China who had prior exposure to AI-supported learning environments. Data were analyzed using IBM SPSS and AMOS through descriptive statistics, reliability testing, confirmatory factor analysis (CFA), structural equation modelling (SEM), and bootstrapped mediation analysis with 5,000 resamples. The findings indicate that ASLE positively predicts innovation ability (β = 0.329, p < 0.001) and career adaptability (β = 0.231, p < 0.001). Innovation ability also positively predicts career adaptability (β = 0.284, p < 0.001). In addition, innovation ability significantly partially mediates the relationship between ASLE and career adaptability (indirect effect = 0.093, p < 0.001; 95% CI [0.05, 0.15]). The model explained 10.8% of the variance in innovation ability and 17.7% of the variance in career adaptability. The study suggests that AI-supported learning in higher education may contribute to students’ future-oriented development not only by familiarizing them with emerging technologies, but also by strengthening innovation-related capability that supports adaptive career readiness. The findings clarify the construct boundary of ASLE and highlight the importance of pedagogically meaningful AI integration in designing learning environments that better prepare students for technology-driven labor markets.
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