Sep 2026· European Journal of STEM Education· 55 references
Abstract
This systematic literature review examines research on artificial intelligence (AI) in mathematics education published between January 1, 2021, and August 1, 2025. Searches of Scopus and Google Scholar identified 922 records; after deduplication, screening, and full-text eligibility assessment, 42 peer-reviewed journal articles and conference proceedings were included. The review used descriptive quantitative summaries and a deductive-inductive thematic synthesis. Two independent reviewers conducted screening (Cohen's kappa = 0.88), and methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). The included literature indicates increasing attention to generative AI, personalised support, feedback, teacher practice, academic integrity, and equity. Evidence for educational benefits varies substantially across study designs and contexts, and technical capability should not be equated with demonstrated classroom effectiveness. Geographic patterns in the selected sample are described without attributing them to regulatory, economic, or infrastructural causes that were not directly tested. Because the 2025 search covered only January through August 2025, publication counts are treated as partial-year data and are not directly comparable to complete prior years. Key limitations include reliance on two databases, English- and Russian-language restrictions, reproducibility constraints in Google Scholar, methodological heterogeneity, and limited long-term evidence. Overall, AI shows potential to support mathematics teaching and learning, but stronger longitudinal and comparative evidence is needed to establish effectiveness, equity, and sustainable implementation.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
