No one-size-fits-all: a study of prompt techniques and large language models to enhance AI’s mathematics educational quality
Sebastian Schorcht; Fabian Anton Müller; Nils Frederik Buchholtz · 2026 · ZDM
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Large Language Models (LLMs) are increasingly discussed as tools for supporting mathematical problem solving. However, existing research predominantly evaluates LLM performance in terms of correctness, while the mathematics educational quality of AI-generated worked-out solutions remains largely unexplored. This study investigates how the mathematics educational quality (a set of quality criteria relating to teaching and learning mathematics) of AI-generated problem solutions can be influenced by model choice and prompt engineering. Four LLMs (Gemini 1.5 Pro (Advanced), Claude 3.5 Son
Abstract by Sebastian Schorcht; Fabian Anton Müller; Nils Frederik Buchholtz, ZDM (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s11858-026-01784-6