Barriers to Generative AI Adoption Among Higher Vocational Students: Understanding Hesitancy and Resistance

Generative Artificial Intelligence (Gen AI) presents transformative opportunities for educational environments, yet adoption within higher vocational education remains constrained by multifaceted barriers. While existing research has explored Gen AI applications across educational domains, limited attention has focused on understanding adoption hesitancy among higher vocational students who face unique considerations regarding practical skill development and workplace preparation. This study employed a quantitative approach using a cross-sectional survey of 312 vocational students across three colleges in China, examining concerns related to academic integrity, security risks, privacy, cognitive dependency, and information reliability. Correlation analysis revealed significant associations between key adoption barriers, indicating these concerns function as interconnected constructs rather than isolated factors. Cluster analysis identified three distinct student typologies: Conditional Acceptors, AI Skeptics, and Receptive Potentials, demonstrating heterogeneity in pre-adoption attitudes despite no prior Gen AI experience. Principal component analysis further confirmed Gen AI resistance is structured along two fundamental dimensions: Cognitive-Security concerns and Ethical-Informational integrity. This study illuminates the complex topology of Gen AI resistance prior to actual engagement, providing educational institutions with insights for developing differentiated approaches that address varying levels of student resistance while ensuring implementation occurs within appropriate ethical, cognitive, and security frameworks.