arXiv:2601.18685v3 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Research syntheses remain scarce and risk being outdated by the time of publication. To address this issue, we present a Living Meta-Analysis (LIMA) on the effects of generative AI-based interventions for learning mathematics. Following PRISMA-LSR guidelines, we continuously update the literature base, apply a Bayesian multilevel meta-regression model to account for nested and cumulative data, and pub
The rapid evolution of generative AI capabilities in education necessitates real-time analysis to prevent research from becoming obsolete before publication, making living meta-analyses crucial.
This living meta-analysis offers continuous, updated insights into the impact of generative AI on mathematics education, providing a dynamic evidence base for educators, policymakers, and AI developers.
The framework for evaluating AI's educational impact shifts from static, potentially outdated studies to a continuously updated, data-driven assessment, allowing for more agile adaptation of pedagogical strategies.
- · AI in education researchers
- · Mathematics educators
- · Generative AI developers
- · Students engaging with AI tools
- · Traditional, slow-publishing research journals
- · Static curriculum development processes
Ongoing, rigorous evaluation of generative AI's effectiveness in learning mathematics becomes standard practice.
Educational institutions accelerate the adoption or modification of AI-powered learning tools based on living evidence.
The development of highly adaptive, personalized AI tutors becomes more effective due to real-time feedback loops from educational outcomes.
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