From AI anxiety to strategic regulation: How university students transform generative AI into a strategic learning resource

Abstract

This study examined how university students engaged with generative AI as a strategic learning resource in academic writing tasks, with particular attention to the role of AI anxiety. Using an explanatory sequential mixed-methods design, survey data and post-task written reflections were collected from 107 university students. The quantitative analysis examined the relationships among AI anxiety, evaluative capacity, ethical awareness, and regulatory engagement, while the qualitative analysis explored how students described verifying, revising, selectively adopting, or rejecting AI-generated content during actual academic writing tasks. The findings showed that AI anxiety was positively associated with verification and revision behaviors, whereas evaluative capacity and ethical awareness were stronger predictors of more active, selective, and reflective forms of AI-supported engagement. Qualitative findings further indicated that effective AI use depended not simply on willingness to use the tool, but on students’ capacity to question outputs, revise selectively, and maintain authorship responsibility. These findings suggest that AI literacy in higher education may be understood less as technology acceptance and more as a form of regulatory competence grounded in evaluative judgment and ethical responsibility.