Jul 2026· Research and Practice in Technology Enhanced Learning· Vol 22, pp. 27· 0 citations
Computer Science
Abstract
Large Language Models (LLMs) are redefining mathematics education, including not only student-facing tutors but also educator-centered tools that automate instructional tasks and support professional development. This systematic review synthesizes findings from 131 empirical and technical studies published between January 2022 and February 2025. Guided by PRISMA protocols and BERTopic modeling, we investigated three key questions: (1) How do LLMs perform on mathematical tasks across varying levels of difficulty? (2) In what ways are LLMs being used to support mathematics educators? (3) What are the limitations and concerns of applying LLMs in mathematics education?
BERTopic identified four dominant themes: (a) evaluation of LLM mathematical reasoning, (b) LLM-supported mathematics education, (c) automation of mathematics educational tasks, and (d) LLM capability in mathematical word problems. Across benchmarks (e.g., GSM8K, SVAMP, MATH), LLM performance is strong on many primary and secondary tasks, especially when combined with remediation approaches such as prompt engineering, fine-tuning, and tool/code-based verification, but reliability decreases as problems require abstract reasoning, multi-step planning, or domain-specific formalisms (notably calculus, statistics/probability, geometry/spatial reasoning, and some non-English settings). Synthesizing findings through an Intelligent Tutoring Systems (ITS) lens, we argue that LLMs are most defensible as generative components within systems that provide instructional control, verification, and student-model inference, rather than as autonomous authorities for mathematical correctness or pedagogy. Key risks include hallucination, limited pedagogical sensitivity, uneven multilingual robustness, and over-reliance that may weaken learners’ independent reasoning. We conclude with implications for evaluation and adoption, emphasizing verifiability, process-sensitive assessment, and educator-centered governance.
Large Language Models (LLM) have become popular tools in education. Therefore, understanding how students interact with LLMs for learning purposes is an important aspect of the responsible design and development of LLM based learning tools. However, existing research has focused on evaluating model performance, specifi...
This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aw...
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Five contributions that examine LLM-enhanced course recommendation, real-time prompting support, automated essay scoring, verifiable online education, and knowledge-augmented automation for higher-education services illustrate how LLMs are increasingly being used as components of educational systems rather than as stan...
Zheng-Dao Li, Jordi Conesa, Moncef Gabbouj et al.· International Journal of Edu...· 0 citations
Large language models (LLMs) are increasingly integrated into educational settings, yet educators lack robust, standards-aligned tools to evaluate their effectiveness in K-12 science contexts. Existing benchmarks predominantly assess general language or advanced scientific reasoning, leaving a critical gap in understan...
Noah L. Schroeder, Yessy Eka Ambarwati, Yu-Ji Zhang et al.· 0 citations
A pedagogically grounded framework that positions educators as pedagogical prompt engineers who scaffold students’ interactions with LLMs through discipline-specific prompting practices is proposed, providing structured classroom exemplars to enhance mathematical reasoning, conceptual understanding, and critical evalua...
Sima Caspari-Sadeghi· Journal of Higher Education...· 0 citations
In education, cognitive models are often representations of knowledge and processes required to solve a task. These models can be used as foundations of pedagogy, curricula, and educational software. Diagnostic classification modeling (DCM) is a family of statistical approaches designed to combine responses to an asses...
Adam Coates· Review of Educational Resear...· 0 citations
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