The role of AI-driven personalised learning in enhancing mathematics problem-solving skills: a systematic review

AI-driven personalised learning is increasingly shaping mathematics education, yet evidence remains fragmented regarding its role in developing learners’ mathematical problem-solving skills. This systematic review examined how AI-driven personalised learning influences students’ mathematical problem-solving skills. A structured search of recent empirical studies (2019–2025) identified 20 eligible investigations, which were analysed thematically. Findings show that AI tools, such as adaptive learning systems, intelligent tutoring systems, and chatbots, can enhance mathematical problem solving by providing tailored feedback, adaptive challenges, and scaffolded support that align with learners’ needs. These benefits were observed across primary, secondary, and tertiary settings, contributing to enhanced conceptual understanding, improved strategic reasoning, and increased learner engagement. At the same time, the review highlights notable variations in effectiveness. Some studies have reported an over-reliance on AI hints, misaligned adaptivity, platform complexity, and limited teacher readiness, which have constrained learners’ development of independent problem-solving skills. Infrastructure disparities and data privacy concerns also emerged as persistent challenges. Despite the growing number of studies on AI in mathematics education, limited systematic evidence exists that focuses specifically on AI-driven personalised learning and its influence on mathematical problem-solving processes. This review addresses this gap by synthesising recent empirical studies and identifying the key mechanisms through which AI personalisation supports or constrains learners’ problem-solving development. In general, the review suggests that AI-driven personalised learning holds meaningful potential for strengthening mathematics instruction when grounded in sound pedagogy and supported by adequate technological and instructional resources. This synthesis contributes evidence-based insights for educators and policymakers aiming to integrate AI responsibly and effectively in mathematics education.