Student AI resistance: status anxiety and trust in Kazakhstani higher education

Purpose While generative artificial intelligence (AI) is increasingly promoted in higher education for personalized learning, automated assessment and content generation, less attention has been paid to students' resistance to its institutional use. This study explores university students' attitudes toward AI integration and examines the relationship between English proficiency and AI acceptance in an English-medium instruction university in Kazakhstan. Design/methodology/approach A quantitative survey was administered to 208 university students. Descriptive statistics, confirmatory factor analysis and multiple-indicator multiple-cause (MIMIC) modelling were used to investigate student acceptance of AI use by both students and teachers and to examine the role of English proficiency while controlling for relevant demographic and educational variables. Findings Students were particularly resistant to AI use in high-stakes evaluative and assessment-related contexts. They also expressed reservations about teachers' use of AI for grading, academic reports and recommendation letters. Higher levels of English proficiency were associated with lower acceptance of AI use by both students and teachers. The findings suggest that AI acceptance may be shaped not only by perceived usefulness but also by concerns regarding fairness, trust and relative academic standing. Originality/value This study contributes to the emerging literature on student resistance to AI in higher education. It contributes to technology acceptance frameworks, including the Unified Theory of Acceptance and Use of Technology (UTAUT), by proposing that AI acceptance may be influenced by perceptions of relative academic positioning. The study also offers practical implications for institutional policy, transparency and AI literacy initiatives in higher education.