# EduIllustrate: Towards Scalable Automated Generation Of Multimodal Educational Content

arXiv: https://arxiv.org/abs/2604.05005 · HTML: https://mlsanity.com/paper/2604.05005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation](https://mlsanity.com/paper/2407.06135/) (2024)
- [Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads](https://mlsanity.com/paper/2412.00127/) (2024)
- [Code2Video: A Code-centric Paradigm for Educational Video Generation](https://mlsanity.com/paper/2510.01174/) (2025)
- [TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding](https://mlsanity.com/paper/2502.19400/) (2025)
- [Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation](https://mlsanity.com/paper/2504.09479/) (2025)
- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [K12Vista: Exploring the Boundaries of MLLMs in K-12 Education](https://mlsanity.com/paper/2506.01676/) (2025)
- [From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization](https://mlsanity.com/paper/2505.16832/) (2025)
- [DiagramIR: An Automatic Pipeline for Educational Math Diagram Evaluation](https://mlsanity.com/paper/2511.08283/) (2025)
- [AGACCI : Affiliated Grading Agents for Criteria-Centric Interface in Educational Coding Contexts](https://mlsanity.com/paper/2507.05321/) (2025)

## Built on by (most cited first, 1 of 1)

- [From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning](https://mlsanity.com/paper/2609.24083/) (2026)

## Abstract (arXiv)

Large language models are increasingly used as educational assistants, yet evaluation of their educational capabilities remains concentrated on question-answering and tutoring tasks. A critical gap exists for multimedia instructional content generation -- the ability to produce coherent, diagram-rich explanations that combine geometrically accurate visuals with step-by-step reasoning. We present EduIllustrate, a benchmark for evaluating LLMs on interleaved text-diagram explanation generation for K-12 STEM problems. The benchmark comprises 230 problems spanning five subjects and three grade levels, a standardized generation protocol with sequential anchoring to enforce cross-diagram visual consistency, and an 8-dimension evaluation rubric grounded in multimedia learning theory covering both text and visual quality. Evaluation of ten LLMs reveals a wide performance spread: Gemini 3.0 Pro Preview leads at 87.8%, while Kimi-K2.5 achieves the best cost-efficiency (80.8% at 0.12/problem). Workflow ablation confirms sequential anchoring improves Visual Consistency by 13% at 94% lower cost. Human evaluation with 20 expert raters validates LLM-as-judge reliability for objective dimensions (ρ ≥ 0.83) while revealing limitations on subjective visual assessment.