Examining the Relationship Between AI Integration, Perceived Ease of Use, And Teachers’ Experience in Enhancing Teaching Effectiveness: The Role of Student Interest

Abstract

Artificial intelligence (AI) is increasingly being integrated into educational practices to support instructional planning, personalized learning, assessment, and student engagement. However, the effectiveness of AI-supported teaching depends not only on technological availability but also on teachers’ perceptions of usability, their professional experience, and students’ interest in learning activities. This article develops a conceptual and methodological framework for examining the relationships among AI integration, perceived ease of use, teachers’ experience, teaching effectiveness, and student interest. Drawing on the supplied literature concerning research design, educational supervision, feedback, collaborative learning, and higher education quality, the study identifies mechanisms through which technological usability and instructional adaptation may influence educational outcomes. A conceptual research model is proposed in which AI integration and perceived ease of use serve as principal explanatory variables, teachers’ experience influences implementation quality, and student interest functions as a potential mediating or moderating factor. The methodology outlines a quantitative, cross-sectional design supported by teacher questionnaires and student engagement measures. Because no empirical dataset was provided, the findings are presented as theoretically derived propositions rather than statistically verified results. The article contributes an integrated framework for future empirical investigation and emphasizes that effective educational AI implementation requires pedagogical alignment, professional development, meaningful student participation, and systematic evaluation.