arXiv AI
Leveraging Generative AI to Design Accessible Interactive Visualizations for Undergraduate Mathematics: A Six-Phase Workflow
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arXiv AI
Aug 10
Curriculum as Code: An AI-Assisted Architecture for Instructional Design in STEM Education
arXiv:2608. 07364v1 Announce Type: new Abstract: Contribution: This paper presents a six-phase AI-assisted instructional design architecture based on the Curriculum as Code paradigm, integrating Generative AI with LaTeX and Python to automate the creation of reproducible, visually consistent, and technically precise materials for STEM education.
arXiv AI
Jul 17
MathCoPilot: An Interactive System for Human-AI Symbiotic Paradigm of Mathematical Research
arXiv:2607. 14582v1 Announce Type: new Abstract: Existing LLM-based theorem provers have achieved impressive results on formal mathematics benchmarks, yet they remain confined to acting as autonomous agents that prove a stated proposition.
arXiv AI
Jun 26
HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
arXiv AI
Jun 4
Characterizing initial human-AI proof formalization workflows
arXiv:2606. 04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been a challenge.
arXiv AI
Aug 21
Gen AI in Proof-based Math Courses: A Pilot Study
arXiv:2509. 13570v2 Announce Type: replace Abstract: With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important.
arXiv AI
Oct 12
MathVis: Can AI Reason through Drawing? A Benchmark for Mathematical Figure Generation
MathVis is a new benchmark that tests large language models (LLMs) on their ability to generate and reason with mathematical figures. It contains 512 tasks, each with a mathematical statement, a reference diagram, executable TikZ or SVG code, a gold intermediate representation, atomic rubrics, and error codes. Initial results show that GPT‑6‑Astra, Claude Fable 5, and Gemini 3.1 Pro Preview achieve pass@1 scores of 31.25 %, 26.56 %, and 22.85 % respectively, yet the models tend to produce outputs dominated by formal languages and graphics programs rather than clear, diagram‑mediated reasoning, often resulting in visually confusing solutions for humans.