Personality traits and emerging digital technology adoption among art students in Chinese universities: A narrative review

Background: Emerging digital technologies, including artificial intelligence (AI), virtual reality (VR), online learning platforms, and cloud computing, are transforming teaching and learning in higher education, yet adoption among students is uneven and shaped by relatively stable individual differences, particularly personality traits. Aims: This narrative review synthesises evidence on the Big Five personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) as predictors of emerging digital technology adoption intention in Chinese higher education, with a particular focus on art students in Shaanxi Province, and proposes a conceptual framework to guide future primary research. Methodology: Drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT2), and the Big Five personality framework, the review thematically synthesises 47 peer-reviewed studies published between 2010 and 2024, identified across six databases (Web of Science, Scopus, PsycINFO, ERIC, China National Knowledge Infrastructure, and Google Scholar) and supplemented by recent generative AI (GenAI) studies located through forward citation tracking. Personality effects are traced through perceived usefulness (PU), perceived ease of use (PEOU), and, for art student populations, hedonic motivation (HM), with resilience and adaptability integrated as personality-adjacent moderating constructs. Results/Findings: Openness and conscientiousness are the most robust positive predictors of adoption intention; neuroticism consistently inhibits adoption through multidimensional AI anxiety, including a creative displacement anxiety unique to art student populations; extraversion is context-dependent and may reverse for solo-use creative tools; and agreeableness operates through social influence rather than as an independent predictor. Recent empirical evidence confirms that these personality effects extend to the generative AI tools increasingly central to art education. Conclusion: The proposed conceptual framework, mapping four conditional indirect paths from the Big Five traits through PU, PEOU, and HM to adoption intention, with resilience and adaptability as supplementary moderators, constitutes the central theoretical contribution of this review. Recommendations are offered for university administrators, educators, and technology developers in Shaanxi and broader China.