Exploring student motivations for AI usage in education: Nisantasi in Turkey, a case study

The purpose of this study wasto examine the main triggers that drive students to adopt artificial intelligence (AI) platforms at university level. Thestudy seeks to build and test a conceptual model of the factors that drive student use of AI-enhanced learning applications. The area of study is broad, addressing intrinsic and extrinsic motivations; Information Seeking, Productivity, Novelty, Enjoyment and Satisfaction dimensions, as well as digital literacy. It uses a mixed methods approach: first, qualitative datawere based on semi-structured interviews with students and experts, and secondly, a quantitative analysis of structured surveys. Data were processed using SPSS and method used was reliability analysis, factor analysis, t-test, ANOVA, correlationand regression analysis. The fit model accounts for 71.2% of variance in students' motivation,with extrinsic factors, and in particular, information seeking, productivity and novelty being the main predictors. Motivationis highly affected by intrinsic elements. Designing an AI platform in line with students' learning circumstances,familiarity with technology and preferences is critical. This study has practical implications for educators,developers, and policy makers who interested to improve learning engagement through AI integration.