Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs
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Abstract (arXiv)
Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases. To this end, we introduce Math Education Digital Shadows (MEDS), a dataset mapping how LLMs reason about mathematics across human- and AI-like personifications. MEDS comprises 28,000 runs from 14 LLMs (i.e., Mistral, Qwen, DeepSeek, IBM Granite, Microsoft Phi, and xAI Grok) generated under human-shadow and AI-assistant conditions. Each record (digital shadow) includes a set of prompts; psychological and sociodemographic metadata; and four mathematics tasks: (i) interviews about relationships with mathematics, (ii) three psychometric questionnaires on mathematics self-efficacy and anxiety, (iii) one cognitive network capturing attitudes towards mathematics, and (iv) 18 high-school mathematics quiz items enriched with reasoning explanations and confidence scores. Analyses of the data show that LLMs exhibit differences in attitudes and performance across human-shadow and AI-assistant modes, including human-like negative attitudes towards mathematics, logical fallacies, and overconfidence in mathematics. As a data resource, MEDS can benefit learning scientists and developers of safer AI tutors in mathematics.
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References · 14
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2023Benchmarking Large Language Models for News Summarizationcited by 444
- 2025MathArena: Evaluating LLMs on Uncontaminated Math Competitionscited by 275
- 2024MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmarkcited by 103
- 2026Ministral 3cited by 88
- 2022Logical Fallacy Detectioncited by 25
- 2025Beyond Final Answers: Evaluating Large Language Models for Math Tutoringcited by 23
- 2024Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITAcited by 17
- 2025Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeekcited by 11
- 2025Measuring and identifying factors of individuals' trust in Large Language Modelscited by 7
- 2026OpenClaw AI Agents as Informal Learners at Moltbook: Characterizing an Emergent Learning Community at Scalecited by 7
- 2025Math anxiety and associative knowledge structure are entwined in psychology students but not in Large Language Models like GPT-3.5 and GPT-4ocited by 3
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