AIlessphobia in Education among University Students: Prevalence and Associations with Deep and Surface Learning Approaches

The rapid integration of generative artificial intelligence (GenAI) tools into higher education has created new learning affordances, but it may also relate to AIlessphobia in education, defined as anxiety and a lack of academic confidence experienced when AI support is unavailable. This cross-sectional correlational research study examined university students’ AIlessphobia in education and its relations with deep and surface learning approaches (and their subdimensions). Data were collected in the spring semester of 2025–2026 from 550 undergraduates at Trakya University using convenience sampling through an online survey; analyses were conducted with 467 students who reported using at least one AI tool. Participants completed the AIlessphobia in Education Scale (AILPES) and the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F). The mean AIlessphobia in education score was 2.30; subscale means were 2.01 for Academic Self-Efficacy Anxiety and 2.67 for Lack of Academic Confidence Without AI. Pearson correlations indicated positive relations between AIlessphobia in education and both deep and surface learning approaches. In stepwise regression, surface motivation (a subdimension of the surface learning approach) showed the strongest standardized relation with AIlessphobia in education, and deep strategy (a subdimension of the deep learning approach) accounted for additional variance, with the final model explaining 11.1% of the variance. Overall, learning-approach dimensions, particularly surface-oriented motivation and deep strategic engagement, were related with AIlessphobia in education, highlighting the importance of supporting metacognitive regulation and balanced, responsible AI use in higher education.