Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2601.18685

LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics

Anselm Strohmaier·Samira Bödefeld·Oliver Straser·Frank Reinhold
View original source ↗Read source document ↗Export metadata ↓

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 Research syntheses remain scarce and risk being outdated by the time of publication. We present a Living Meta-Analysis (LIMA) on the effects of generative AI-based interventions for learning mathematics. We update the literature base every two months and apply a cumulative Bayesian multilevel meta-regression model. This paper reports results from the fifth version, including 34 studies, 7 of which were newly included since the fourth version. Results indicate an increasingly positive effect (g = 0.57, CrI [0.36, 0.79]) and no evidence in favor of a publication bias. Moderator analyses indicate that effects vary by integration mode and intervention duration: Larger effects were observed when generative AI supplemented teacher instruction and in longer interventions. Moderate evidence for stronger effects was observed in studies involving younger learners and more recent technology. For other moderators, evidence is still inconclusive.

Explore related subjects

math.HOcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anselm Strohmaier, Samira Bödefeld, Oliver Straser, Frank Reinhold. 2026-09-11. LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics. https://arxiv.org/abs/2601.18685

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

On the Reconstruction of SAS from Other Triangle Congruence Criteria, Part II: Eliminating the Pons Asinorum

In the first part of this work we showed that, within a Hilbert plane deprived of the Side-Angle-Side axiom, the Side-Angle-Angle criterion, together with a ray correspondence principle [\textbf{RCT}], the existence of angle bisectors [\textbf{AB}], the congruence of supplements of congruent angles [\textbf{SA}], and the Pons Asinorum [\textbf{PA}], suffices to reconstruct SAS. We left open the question of whether [\textbf{PA}] is genuinely required alongside the other three principles, noting only a qualitative asymmetry in the nature of the principles involved. In this second part we answer this question: we show that \begin{equation*} \textrm{SAA},\ [\textbf{RCT}],\ [\textbf{AB}] \;\vdash\; [\textbf{PA}], \end{equation*} so that [\textbf{PA}] is redundant among the hypotheses of our main theorem, which improves to \begin{equation*} \textrm{SAA},\ [\textbf{RCT}],\ [\textbf{AB}],\ [\textbf{SA}] \;\vdash\; \textrm{SAS}. \end{equation*} The proof adapts an argument recently given by Donnelly, who reconstructs SAS from SAA together with an angle addition axiom and the existence of angle bisectors.

math.HO↗
arXiv2026

Chebyshev and garment cutting. Debunking some myths

In {\tt 1878}, Pafnuty Chebyshev presented to the {\it Association fran\c caise pour l'avan\-cement des sciences} {\it [French Association for the Advancement of the Sciences]} an article \cite{Chebyshev1878} dealing with garment cutting. According to Chebyshev himself, his interest was sparked by a lecture given by Édouard Lucas that he had attended in {\tt 1876} \cite{Lucas1876}. There is a second story on the origin of Chebyshev's interest in garment cutting according to which in the 1850s, being short of money, Chebyshev got himself a job as a consultant to a clothing factory. At the time of the Crimean War (1853-1856), there was a great demand for uniforms. Chebyshev was allegedly asked to optimize the use of fabric, and it was there that his interest in garment cutting was born. This second story appears to have its origin in a post by Clive J. Grant to MacTutor in 1996 \cite{Grant1996}. However, this contribution contains no references, and no other source of information that I have found offers any first-hand documentation to support this story. Our conclusion is that this second story is a fabrication, invented out of whole cloth.

math.HO↗
arXiv2026

Mathematics Graduate Training in the Age of AI

Generative AI changes the conditions under which graduate mathematics is learned, assessed, written, and defended. The central claim of this paper is that mathematics graduate programs should respond to the moment by clarifying what graduate mathematics education is trying to teach and assess. In most ways, the goals of mathematics education have not changed. Rather, with changing tools it has become more essential than ever to make clear the goals of mathematical training. We use the term mathematical judgment to refer to the capacity to evaluate mathematics (e.g., claims, definitions, examples, proofs, analogies, computations, uses of tools, research directions) as mathematically sound, useful, well-posed, and appropriately justified. The recommendation is to center training on mathematical judgment, and we examine possible policies for graduate programs in Mathematics to this end.

math.HO↗