The purpose of this study was to examine the main triggers that drive students to adopt artificial intelligence (AI) platforms at university level. The study 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 data were 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, correlation and 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. Motivation is 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. • AI tools enhance engagement and learning in higher education. • Digital literacy strengthens motivation to use AI in learning. • Intrinsic and extrinsic factors drive AI-based student motivation. • Novelty and enjoyment impact student engagement with AI tools. • AI-supported learning improves outcomes and student satisfaction.
AL-Sayid et al. (Mon,) studied this question.