The artificial intelligence (AI) in learning is being increasingly applied for the personalization of learning, but the psychological implications of AI-powered platforms on students, particularly in areas with poor infrastructure, are yet to be investigated. Although there are studies that mostly concentrate on the performance of the systems and learning gains, the psychological experiences of students are still not well understood. These experiences include motivation, self-regulation, and anxiety related to algorithms. To complement this deficiency, this model examines the impact of AI-based personalized learning on students’ motivation, engagement, and algorithm-related anxiety. A convergent parallel mixed-methods design was used, combining quantitative analysis and qualitative thematic analysis of learner experiences. The study is grounded in Self-Determination Theory, Technology Acceptance Models, and Sociotechnical Systems Theory. Findings indicate that AI-based personalized learning enhances student motivation and self-regulated learning, while perceived system usability influences engagement and anxiety. In addition, institutional and infrastructural differences moderate these effects. The study highlights the psychological and contextual implications of AI in education and offers insights for improving personalized learning systems. The digital infrastructure plays a crucial role in reducing students’ anxiety toward algorithm-driven systems by providing stable access, reliable connectivity, and smoother interaction with AI-based learning platforms. This improved technological environment enhances student engagement and active participation in AI-supported learning activities. The findings highlight that when infrastructure is robust, learners are more comfortable using AI tools, leading to better learning experiences and outcomes.
Yue Wang (Wed,) studied this question.