From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization
Authors: Haonian Ji
Shi Qiu
Siyang Xin
Siwei Han
Zhaorun Chen
Dake Zhang
Hongyi Wang
Huaxiu Yao
Venue: ICLR 2026 Poster (2026)
Keywords: education
agent
benchmark
llm
application
visualisation
Primary area: datasets and benchmarks

Abstract: While foundation models (FMs), such as diffusion models and large vision-language models (LVLMs), have been widely applied in educational contexts, their ability to generate pedagogically effective visual explanations remains limited. Most existing approaches focus primarily on textual reasoning, overlooking the critical role of structured and interpretable visualizations in supporting conceptual understanding. To better assess the visual reasoning capabilities of FMs in educational settings, we introduce EduVisBench, a multi-domain, multi-level benchmark. EduVisBench features diverse STEM problem sets requiring visually grounded solutions, along with a fine-grained evaluation rubric informed by pedagogical theory. Our empirical analysis reveals that existing models frequently struggle with the inherent challenge of decomposing complex reasoning and translating it into visual representations aligned with human cognitive processes. To address these limitations, we propose EduVisAgent, a multi-agent collaborative framework that coordinates specialized agents for instructional planning, reasoning decomposition, metacognitive prompting, and visualization design. Experimental results show that EduVisAgent substantially outperforms all baselines, achieving a 40.2% improvement and delivering more educationally aligned visualizations.

Published as a conference paper at ICLR 2026
FROM EDUVISBENCH TO EDUVISAGENT: A BENCH- MARK AND MULTI-AGENT FRAMEWORK FOR REASONING-DRIVER PEDAGOGICAL VISUALIZATION
Haonian Ji Shi Qiu-", Siyang Xin' Siwei Han"
Zhaorun Chen², Dake Zhang", Hongyi Wang', Huaxiu Yao- UNC-Chapel Hill, UUniversity of Chicago, 'Rutgers University
ABSTRACT
While foundation models (FMs), such as diffusion models and large vision- language models (LVLMss, have been widely applied in educational contexts, their ability to generate pedagogically effective visual explanations remains limited. Most existing approaches focus primarily textual reasoning, overlooking the critical role of structured and interpretable visualizations in supporting conceptual understanding. To better assess the visual reasoning capabilities of FMs in educa- tional settings, we introduce EduBeeBe a multi-domain, multi-level benchmark. EduViseench features diverse STEM problem sets requiring visually grounded solutions, along with fine-grained evaluation rubric informed by pedagogical theory. Our empirical analysis reveals that existing models frequently struggle with the inherent challenge of decomposing complex reasoning and translating it into visual representations aligned with human cognitive processes. To address these limitations, we propose Edu Viseen,geet,, a multi-agent collaborative framework that coordinates specialized agents for instructional planning. reasoning decompo- sition, metacognitive prompting. and visualization design. Experimental results show that Edu VisAgent substantially outperforms all baselines, achieving a 40.2% improvement and delivering more educationally aligned visualizations. Edu Vis- Bench and Edu VisAgent are available at www.ooo////ooaa////ooooio VisBench and github.com/aiming.e
lab///i lab/Edu VisAgent.
INTRODUCTION
"To truly teach not to tell the answer, but to illuminate the path." "
While foundation models (FMs), such diffusion models and large vision-language models (LVLMs), have been extensively adopted in educational do- mains (Chu et al., 2025; Wang et al., 2024), including pedagogical agents providing automated classroom assistance and science learning agents offering tex- tual explanations of problem- moovvng processes (Wu et al., 2023), their applications have predominantly focused on text-based interactions (Wu al., 2023; Xu et al., 2024). However, in education, especially K-12 settings, creating compelling visualizations crucial for cognitive comprehension and overall learn- ing effectiveness (Presmeg, 2006). Despite its im- portance, there currently limited understanding of how FMs can effectively generate visually grounded elements (e.g., diagrams, interactive education tools, illustrative graphics) support the pedagogical illustration of problem-solving processes.
doorde peoducing
A- bome.
Vauullae the probbem- solking proceaa.
Figure 1: GPT-40 fails to illustrate its problem-solving with high-quality, logical, and explanatory visualization.
Equal contribution. Correspondence to: haonianj @une.edu, edu, huaxiu C unc.ec.d

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Published as a conference paper at ICLR 2026
Currently, generating visually grounded elements for pedagogical reasoning poses several challenges: (1) decomposing complex reasoning into representable steps that align closely with human cognitive processes is non-trivial (Yang et al., 2024; Chen et al., 2024d); (2) precisely producing visual aids for each sub-step to optimally support learners is challenging (Hong et al., 2025); and (3) different educational domains require distinct visualization styles and formats, which makes consistent and adequate visual aid delivery difficult (Pandey & Ottley, 2025). This difficulty stems not just from technical rendering challenges, but from the complex task of translating abstract pedagogical concepts into intuitive visual narratives. Addressing these obstacles first requires a picture of how current FMs perform, that future models can be purpose-buil built to close the gaps. Consequently, a comprehensive evaluation platform is critical for systematically assessing FMs on visual pedagogical reasoning.
We introduce Edu VisBench, a multi-domain, multi-level benchmark for evaluating the capacity of FMs to gener- ate pedagogically effective, step-by-step visual reasoning. Edu VisBench comprises structured problem sets across diverse domains, each requiring multimodal-centric rea- soning and solutions that prioritize visualization princi-
Goometry
Calculus
ples such as interpretability, cognitive alignment, and instructional clarity to achieve high evaluation scores. We further develop a detailed rubric enabling multidimen- sional assessment of Al-generated visual outputs across five pedagogical criteria: contextual relevance, visual clarity, multimodal coherence, reasoning support, and interactive engagement.
Stats
Mathenatics
Algebra
732
Atomic
Phynice 207
Optica
LinAlg
Chomistrr
Thermo
Reartioa
Propertion
Expertmenta
Mechanica
Eleareta
E&M
Utilizing this benchmark, we conduct extensive evalua- tions on a variety of FMs and agents. Our findings reveal that although current models achieve predominantly cor- rect step-by step textual analyses, they frequently fail to generate useful or faithful visualizations, as depicted in Figure 1. Specifically, our systematic analysis high- lights recurring challenges including (I) semantic mis- alignments between textual explanations and visual components, (2) omissions of critical steps within rendered diagrams, and (3) structural inconsistencies in code-based visual outputs, collectively undermining accuracy, clarity, and interactivity. These shortcomings collectively compromise the pedagogical utility of the generated content, often leading to more confusion than clarity for the learner.
Figure 2: Dataset distribution of Edu Vis- Bench. Each domain encompasses var- ious sub-domains, collectively covering 15 comprehensive pedagogical scenarios.
To address these limitations, we introduce a multi-agent collaborative framework, Edu VisAgent, designed to simulate the complete learning journey-from initial problem exposure deep concep- tual understanding. Specifically, a central planning agent orchestrates five specialized expert agents dedicated to visualization design, cognitive scaffolding, and metacognitive regulation. synthesis module then integrates these expert outputs into interactive, personalized learning webpages tailored specifically to human learners. Experimental results demonstrate that our proposed method Edu Vis- Agent achieves an average improvement of 40.2% over all baselines. The gains stem from modular specialization across agents and their collaborative integration into a single learning webpage.
2
EDUVISBENCH BENCHMARK
2.1 OVERVIEW
In this section, we introduce Edu VisBench, a benchmark designed to evaluate the capability of models to generate logical and explanatory visualizations for educational purposes. As shown in Figure 2, Edu VisBench comprises 1,154 STEM questions across three academic subjects and 15 distinct domains, organized into three levels of difficulty. In addition to assessing accuracy in step-by-step problem solving, Edu VisBench places particular emphasis model's ability to communicate the reasoning process clearly and hisuallyheelping students understand problems through structured, interpretable visual outputs, illustrated in Figure 3.
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Mathematics
Chemistry
Physics
Question
Question
Question
the blank make the equation true. 2 =
propertion and eftects addreas whother Orygen Busl, The peopoction
force of 50 is applind to balock. What the acoslecation of the block?
the sir in approemmannly 21%.
Viaualization
Visualization
Visualization
Vieasl (Bvidanne: 6rS, Viseal Dedige A/A
Gaordiastion: L/f, feorring Gerdiwaen fel,
Yasal Daidance: 5/5, Vaxad Design 4.7
Vasal Gaidanze: B15, Vanad Design 47% Cooedtralso: 57% leeeiin Guslasees 6/9,
Total
brorerndtyy: 3.1
Total 8core:88%
Total Score: 92%
Figure 3: Representative examples from Edu featuring questions from Maths, Chemistry, and Physics alongside their corresponding high-scoring visual explanations. These interactive visual- izations, generated by multi-agent system Edu VisAgent, exemplify well-designed, pedagogically effective outputs for STEM problems.
Specifically, Edu VisBench adopts a multimodal setting in which models are provided with both textual and visual inputs and are tasked with producing diverse output formats, including interactive web pages and visual diagrams. Beyond evaluating the correctness of final answers, we introduce detailed evaluation framework that assesses visualization quality across five dimensions: (1) the logical sequencing of visual elements, (2) the structural richness of the visuals, (3) semantic alignment with the underlying subject matter, (4) the clarity and guidance provided for problem-solving, and (5) the level of interactivity and engagement. In the following subsections, we describe our dataset curation process and the design of the evaluation rubric detail.
2.2 DATASET CURATION
Edu VisBechh is built from several high-quality public educational resources that we carefully curated, translated, and adapted support multimodal visualization learning tasks. Specifically, the chemistry questions are sourced from the C-MHChem C-MHHChem-Benchmark (Zhang et al., 2024), originally presented in Chinese and meticulously translated into English with careful attention scientific accuracy and terminology. The physics questions are drawn from the segh--schol-pphyee which includes range of conceptual and quantitative exercises suitable for secondary-Ieve learn- ers. The mathematics component combines easy-level problems from the Illustrative Mathematics curriculum with medium- to hard-level questions selected from the MATI--00 (Lightman al., 2023) dataset. Furthermore, each domain encompasses diverse sub-domains collectively covering 15 comprehensive scenarios, as illustrated in Figure 2. All data sources were standardized into a unified format and consolidated to enable consistent and comprehensive evaluation across subjects.
(Rohith, 2023) dataset,
2.3 EVALUATION METRIC
In this subsection, we detail the performance evaluation rubries in EduVisBench.
Evaluation Dimensions. To evaluate the quality of generated visualizations in supporting student understanding, we introduce scoring metric grounded five pedagogically motivated dimensions: (1) Context Visualization: evaluates how clearly the visualization situates the problem within relevant context; (2) Diagram Design: assesses the clarity, accuracy, and effectiveness the diagrams used to represent information; (3) Text-Graphic Integration: measures the coherence between textual explanations and visual elements, ensuring mutual interaction; (4) Thought Guidance: examines the extent which the visualization supports reasoning processes and highlights critical
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thinking steps; (5) Interactivity: evaluates whether and how the visualization invites students' engagement, reflection, or active manipulation. Each dimension captures a distinct aspect effective multimedia learning, with detailed rubries provided in Appendix A.1 to guide the scoring process.
Evaluation Protocol. As shown in Figure 4, models are provided with visualization prompt together with question and asked to generate visual outputs. To enable fair comparison across het- erogeneous outputs, we first canonicalize every model result to a raster image prior to scoring. This standardization is a crucial step that ensures all systems are evaluated on level playing field, independent of their native modality or file format, and prevents format-specific rendering artifacts from biasing the assessment. Visuals produced directly SVG or PNG are used as-is. Web pages (HTML or Next.js) are rendered in a headless browser and captured as screenshots of the primary view; when lightweight interactivity is present (e.g., buttons, tabs, or toggles), we system- atically traverse the reachable states and retain representative screenshot per state. All resulting images are then evaluated by GPT-4o along five dimensions defined in Appendix A.2 to com- pute an overall performance score. Each dimension rated on 0-5 scale; the ratings are summed (0-25) and, when appropriate, normalized to a percentage to yield the final overall score.
Vircalication Promptt
Queation
VZ
Dithasion Modelx
12/1
Visual Agent
Render
Vistalioation
Scrunahci
Iragge
Evaluation
CPE46
Toual Quilamn
Toral Soornw
Figure 4: Workflow for evalua- tion.
3 EDUVISAGENT
Using Edu VisBeenh we systematically evaluate the performance of existing text-to-image models and LVLMs (see detailed results in Table in Section 4). We find that most models perform poorly, with average scores below 50 on 0-100 scale. This performance gap motivates moving beyond single-model architectures: decomposing complex reasoning into visual representations that support human learning is genuinely hard problem that no single model currently handles well.
To address these challenges, we propose a multi-agent system, Edu ViuAig,gent, inspired by pedagogical theories and designed to emulate the division of labor and collaborative reasoning found in expert instructional design. Edu VisAgent consists of five specialized yet interdependent agents: a Task Planning Agent, which structures the instructional objective; a Conceptual Mapping Agent, which extracts and organizes key information; a Reasoning Decomposition Agent, which constructs step- by-step mooblem-solvingg logic: Metacognitive Reviewer, which encourages summarization and learner reflection; and a Visualization Agent, which generates appropriate visual representations. This design introduces modularity and pedagogical interpretability by embedding distinct instructional roles directly into the agent workflow. The overall operation of Edu proceeds in two stages: (I) instructional flow construction and (2) collaborative solution generation, as detailed below.
3.1 INSTRUCTIONAL. FLOW CONSTRUCTION
The first stage of Edu VisAAnt focuses on formulating well-structured instructional task based on the original problem. A key challenge lies in analyzing the underlying reasoning structure, identifying implicit logical dependencies, and associating each reasoning step with relevant conceptual knowledge. To address this, we employ the Task Planning Agent, which systematically organizes the problem into an instructional format suitable for multimodal visualization. Its main functions include: (I) breaking down the problem into coherent subgoals, (2) clarifying the reasoning expected at each step, (3) aligning each step with domain-specific principles formulas, and (4) anticipating potential student misconceptions or cognitive needs. This structured formulation provides pedagogically grounded foundation that guides the downstream agents in generating accurate, educationally grounded visual explanations.
3.2 COLLABORATIVE SOLUTION GENERATION
In this stage, Edu Edu VisAgent executes the instructional task constructed by sequentially activating a set of specialized agents, each responsible for completing a specific aspect of the task. As shown
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in Figure 5, these agents operate in coordinated manner enhance the coherence of instructional logic, improve the clarity of visual representation, and ensure alignment with educational objectives. Specifically.we detail each agent follows.
Conceptual Mapping Agent. This agent is respon- sible for extracting and organizing the com- ponents of the input problem. Drawing on the Poacrete-Repreeesntttioa------i
? Oueetion
Task Planning Agent
(CRA) instructional model (Nugroho & Jailani, 2019), it classifies information into three categories: concrete entities, representational elements, and abstract constructs. This structured classifi- cation helps bridge the gap between the concrete elements of problem and the abstract principles required to solve it. This progression from concrete to abstract is particularly valuable for an AI system, as it provides a structured path- way to ground complex concepts in relatable terms before generating symbolic representations. The agent conducts fine-gre fine-grained categorization and semantic summarization support downstream visualization modules.
subgoals
expectation
formulaa
Conceptual Mapping Agent
Reasoning
Metacognitive Reviewer
Decompositin Agent
Visualization Agent
Interactive
Wabpage
Figure 5: The structure of Edu VisAgett
Reasoning Decomposition Agent. This agent decomposes complex problems into manageable subcomponents and provides step-specific instructional guidance. It applies the memory-orientee FOPS strategy (Miller & Cohen, 2020)-find the problem type (e.g.. equation solving, conceptual reasoning, commonsense application, or graphical interpretation), organize the structure via equations or diagrams, plan the solution path, and solve the task. Based on the decomposed steps, the agent also identifies critical instructional points that require additional support, especially those that benefit from visual scaffolding or interactive guidance.
Metacognitive Reviewer. Grounded in metacognitive theory (Schraw & Moshman, 1995), this agent supports learners in monitoring their comprehension and reasoning processes. It generates reflective prompts that foster self-questioning and self-correction, encouraging learners to evaluate the soundness of their problem-solving approaches.
Visualization Agent. This agent is responsible for constructing the "visual guidance" component of the instructional output. Instead of relying on decorative visuals, it emphasizes the use of abstract yet pedagogically effective representations- such as number lines, bar charts, schematic object illustrations, graphic organizers, sketch diagrams, and structured data tables. The agent ensures that each visualization is tightly aligned with the underlying abstract concept being taught. All visuals are rendered using the vθ (Vercel, 2025) system for web-based deployment.
EXPERIMENTS
This section outlines the experimental setup for benchmarking various foundation models on Edu- VisBench. We evaluate Diffusion Models, LVLMs, specialized visualization agent (v0), and our proposed EduVisAgent. Our investigation seeks address the following key questions: (1) How proficient are existing models at generating high-quality, explanatory visualizations within Edu EduVisBench? (2) Can the proposed Edu VisAgent system outperform current models? (3) What distinct performance patterns emerge across different model architectures, academic disciplines, and evaluation dimensions in Edu Visnehhenhh?
4.1 EXPERIMENT SETUP
Baseline Models. Our experimental evaluation encompasses range of FMs, categorized as follows:
(1) Image Generation Models: This category includes Flux.1-des (Labs, 2024), Stable Diffusion 3.5
Large (SD3.5) (IT Admin, 2024), and Stable Diffusion XL Base 1.0 (SDXL) (Podell et al., 2023).
These models are tasked with generating static images directly from textual visual inputs. (2)
Large Vision-Language Models (LVLMs): We evaluate Deepseek- VL2 (Wu et al., 2024), GLM-4V.
9B (GLM et al., 2024), MiniCPM-V2.6 (Yao et al., 2024),
-as---Insttutt-55000 (Mis- tral AI, 2025), Phi-3.5-Vission Instruct (Abdin et al., 2024), Mhii--Multimddal-mmee -nntruat-Instruct (Abouelenin
al., 2025), Qwen2.5-VLL-2B (Team, 2025), GPT-4o (Hurst et al., 2024), Claude 3.7 Sonnet (An-
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Table Performance of Diffusion Models, Large Vision Language Models and on Edu EdunisBench.
Maths Medium
Physics Medium
Chemistry Easy Medium Hard
Avg
Method
Vis. Type
Easy
Hard
Easy
Hard
Diffusion Model
Image Image
13.8
13.4
13.2
11.7
8.5
10.0
20.0
16.6
16.0
13.8
SD3.5 SDXI.
17.3
20.3
18.8
16.8
13.0
12.0
21.7
34.0
18.4
Image
17.3
23.3
25.5
18.9
15.4
24.0
33.6
30.2
24.0
21.8
Large Vision Language Model
Deepseck VI.2
Webpage
20.3
17.1
15.7
17.9
17.0
20.0
16.4
13.8
14.0
17.5
GLM-4V-9B
Webpage
22.3
21.1
19.4
24.5
21.5
24.0
22.3
21.5
16.0
21.9
MiniPPM-V-2.6
Webpage
24.1
17.3
15.5
19.1
17.4
20.0
14.5
15.2
12.0
19.3
Mistral-Small-3.1
Webpage
29.1
31.6
32.2
32.3
33.5
20.0
30.6
27.5
24.0
30.2
Phi-3.5
Webpage
25.3
20.7
19.1
21.2
19.5
12.0
20.0
18.6
20.0
21.8
Phi-4
Webpage
26.1
25.1
22.9
27.8
25.5
24.0
31.2
27.5
12.0
26.4
Owen2.5-VL-77.
Webpage
24.3
18.1
15.8
19.7
17.1
24.0
18.2
16.4
12.0
20.0
Claude 3.7 Sonnet
SVG
61.2
26.7
23.6
18.5
16.9
14.0
47.5
47.2
18.0
42.0
Claude 3.7 Sonnet
Webpage
56.2
57.5
55.6
44.8
42.6
24.0
61.1
60.6
64.0
54.6
GPT-4o
Webpage
47.6
39.3
37.9
25.7
24.2
24.0
34.3
32.6
36.0
38.1
GPT-4o
SVG
36.1
19.7
19.5
13.0
12.8
4.0
30.0
27.5
22.0
26.3
Gemini 2.0 Flash
Webpage
46.9
9.5
15.7
31.7
26.5
24.0
32.0
25.8
30.0
43.6
Visualization Agent
v0
Webpage
63.0
37.6
47.2
53.3
58.5
52.0
74.7
52.8
68.0
58.2
thropic, 2025), and Gemini 2.0 Flash (Mallick & Kilpatrick, 2025). These models are prompted to generate SVG or HTML code, which then rendered into visual outputs for evaluation. (3) Specialized Visualization Agent: We also assess vθ (Vercel, 2025), AI agent specifically designed to create interactive web pages based on instructional content. This diverse selection of models was chosen represent the current state-of-the-art across different architectural paradigms.
During evaluation, all generated visualizations are standardized into image format. For interactive web pages containing buttons, automated script navigates through all accessible sub-pages, capturing individual screenshots of cach. This automated approach ensures that the evaluation both scalable and free from subjective human bias during the rendering process. Performance is assessed using the evaluation metric described in Section 2.3, where GPT-4o scores the visual outputs based on predefined rubrics, assigning score from 0 for each of the five dimensions. The cumulative score (maximum 25 points) is then normalized to O- 0-100 scale for standardized reporting and comparison.
Evaluation Setups.
Reliability of GPT-based Scoring. To validate the reliability of our au- tomatic judge, we compared GPT- based evaluations with human eval- selected
Table 2:
Cosine similarity and mean squared error across subjects. Math the average Math500 and IllustrativeMath, each with 50 samples.
uations. Specifically, 50 samples from each subject cate- gory (Chemistry, Math, and Physics), and had both GPT and human eval- uators independently rate them. Hu- man evaluators were undergraduate students from top universities. We measured agreement using Cosine Similarity and Mean Squared Error (MSE). As shown in Table 2, high
Metric
Chemistry
Math
Physics
Average
Cosine Similarity t
0.9742
0.9557
0.9666
0.9655
MSE
0.3895
0.7093
0.6118
0.5702
cosine similarity 0.9655 and MSE 0.5702 across subjects indicates negligible practical discrepancy.
4.2 BASELINE BENCHMARKING
The performance of all evaluated baseline models is detailed in Table 1. Across all evaluated models, the average scores indicate significant room for improvement. Diffusion Models generally exhibited the lowest performance, with average scores ranging from 13.8% (Flux.I-dev) to 21.8% (SDXL). This suggests that direct static image generation, while capable of producing visual elements, struggles

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substantially with the nuanced requirements of explanatory and guiding visualizations for complex logical problems in our benchmark.
LVLMs typically scored between 17.5% (Deepseek VL2) and 30.2% (Mistral-SSaall-3.l Notable exceptions include Gemini 2.0 Flash (43.6% and Claude 3.7 Sonnet; the latter's significantly better performance with Webpages (54.6%) over SVG (42.0%). GPT-4o also showed a preference for Webpage generation (38.1 %) over SVG (26.3%), suggesting that prompting advanced LVLMs for structured interactive webpages can yield more effective visual explanations. Nevertheless, even these top-tier LV LLM face considerable challenges in consistently meeting all of evaluation criteria. The visualization agent vO. specifically designed for webpage generation, achieved the highest average score among all baseline models at 58.2%. This result highlights the advantage of a specialized agent in this task over more general purppss FMs.
Table 3: Overall comparison of models: left is our Edu VisAAent performance, right is the bar chart. Edu VisAgent achieves the highest average score among all models.
(a) Performance of our Edu Aint Edu VisBench. (h) Comparison of average score across all models.
Method
Vis. Type
Easy
Medium
Hard
Avg
Avg Score
Averuge Score Comparison
Maths
90.2
64.5
65.0
M
Edu Agent
Wehpuge
Physics 81.7
81.6
85.3
84.0
20
10.
20
Chemistry
diyo denter
Gol Mecont
69.0
76.3
76.0
Closai City
4.3 PERFORMANCE ANALYSIS OF EDU VISBENCH
Building upon the insights gained from the baseline evaluations, we assessed our proposed multi- agent system, Edu VisAgent. The results in Table demonstrate a substantial leap in performance for generating explanatory and logically valuable visualizations for STEM problems. Edu VdsAgnt achieved an impressive overall average score of 81.6%. Specifically, Edu Vis.Aeett surpasses the best-performing baseline vθ (58.2%), by 23.4 percentage points (40.2% relative), showing the benefit of the multi-agen agent architecture and its integration of educational methodologies. Compared to the best performing LVLM (Claude 3.7 Sonnet Webpage at 54.6%) and the top diffusion model (SDXL at 21 ).). the advancement offered by Edu AisAgent is even more pronounced. These results clearly indicate that the design principles underlying Edu VisAgent, which incorporate multi-agent structure and pedagogical strategies, effectively address many of the limitations observed in existing generative models.
4.4 CASE ANALYSIS
To further illustrate the limitations of existing baselines and how our approach addresses these challenges, we present two case studies in Figure 6. On the left, for a chemistry question, the GPT-44-generated solution lacks intuitive visualization of the chemical processes, resulting in fragmented information without visual guidance-reflectee in a low score of just 28%. In contrast, Edu VisAgent begins by displaying background images of the relevant chemical elements, activating students' prior knowledge. This strategy effectively connects abstract chemical concepts to tangible, everyday experiences, a well-established method for enhancing comprehension and retention. It then contextualizes each of the four answer options with real-world scenarios, thereby enhancing students' understanding of the underlying chemical transformations.
Conversely, for the Carnot cycle efficiency physics problem (right side of Figure 6), the Gemini solution presents a single, flawed chart. Its depiction of 300K and 400K temperatures with identical heights introduces visual misinformation, failing accurately represent data differences and thereby diminishing its pedagogical value. In stark contrast, Edu VisAgent employs multi-agent collaborative approach: it first generates concrete factory scene to activate students' working memory of the "heat engine" concept. Subsequently. it constructs an accurate Carno cycle diagram and offers a step-by-step problem breakdown, fostering clear conceptual understanding. Crucially, Edu Vis
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Lack of Visual Guidance
Visual Information Mispresentation
Question
GPT-40
Question
Btamag daeage www.ggg dhaniol O Siiting Soee D Pepplt Serting
Carner barmen 23'C and 137"C What to ellikney??
Viei Dertemen 2-t Shaa Pustye 1rt Total
Vana Goddace 25, Yark, Deagee 5/5 Total Score: 28%
The maga hala Bustrute the chemical inttrcctionll approsci, and otfeii as
The image contaie only one chart and sakues teephts 5000, 600k sbertioal
not rellccing foe offinnec dkik
EdeVisAgent (Ours)
EdeVinAgent (Ours)
Figure 6: Baseline models versus our Edu VisAgent. These examples clearly demonstrate the often poor output quality of baseline models, contrasting sharply with the high-quality, effective visualizations produced by Edu VisAgent.
Desige
Thongga Cuiderce
Matral Sreal-3
37 Wetagg
Genrs (Pidqppp
baMaagee
Figure 7: Per-dimension performance comparison across the five evaluation dimensions.
provides interactive visualization components, enabling users dynamically adjust temperatures via sliders and observe real-time changes in heat engine efficiency. This interactive element transforms the learner from passive observer into an active participant, which known to deepen engagement and learning. This interactive engagement significantly facilitates higher-order thinking skills.
Overall, through coordinated multi-agent optimization of image design, instructional structure, and learning pathways, EduVisAgent significantly outperforms traditional single-model approaches in accuracy, guidance, and interactivity.
4.5 FER-DIMENSION ANALYSIS
Figure 7 reveals distinct performance profiles for eight high- perfoorfffrming evaluated models. In Context Visualization and Diagram Design, most baselines, including SDXLL, Claude 3.7, and vo, exhibit moderate to low scores, often struggling with providing rich situational cues or pedagogically sound visual structures, especially for complex problems. vθ and Claude show relatively better capabilities in Text-Graphic Integration and Thought Guidance compared to other FMs, which generally offer minimal support these areas. However, all baseline models, including v0, are significantly limited in the Interactivity dimension, primarily due to their output format (static images/SVGG or less dynamic webpages). contrast, our Edu VisAgent demonstrates consistently strong performance across all five dimensions. It particularly excels in creating rich context visualizations, well well-structured diagram designs, and ensuring seamless text-graphic integration. Edu EduVisAgen also leads by wide margin in Thought Guidance and Interactivity- two dimensions where all basclines score lowest- inniittinniniinngg that structured agent coordination is particularly important for these harder-to-achieve pedagogical properties.
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5 RELATED WORK
LLM for Pedagogical Assistance. Foundation models (FMs), including diffusion models and large vision- -siionlanguage models (LVLMs), have been increasingly adopted in educational contexts (Chu et al., 2025; Wang et al., 2024) to support teaching and classroom interactions. EduAgent (Xu et al., 2024) and Teachtune (Jin al., 2025) enhance the problem-solving process through automated simulations of student-tcache SEFL (Zhang et al., 2025) and PROF (Nair al., 2024) synthesize immediate, on-demand feedback to support large-scale instructional scenarios. Domain-spednt -peeain- agents such as MathChat (Wu et al., 2023), NEWTON (Wang et al., 2023b), and MEDCO (Wei et al., 2024) further provide textual explanations tailored to scientific and medical education. While these systems address diverse pedagogical needs, their focus remains largely on text-based interactions (Wu et al., 2023; Xu et al., 2024; Cui et al., 2024), overlooking the critical role of visualization in fostering conceptual understanding and improving learning outcomes (Presmeg, 2006). While valuable, these text-centric systems do not address the large body of educational research highlighting the unique cognitive benefits of visual learning. Despite its pedagogical importance, the capacity of FMs and agents to generate logical, explanatory visual illustrations remains underexplored. Edu is the first benchmark designed to evaluate FMs' ability to produce pedagogically effective, step-by-step visual reasoning, covering 15 visually grounded educational scenarios with multi-level problem sets and multmmodal-entric solutions.
dialogues, collaborative learning, and task-oriented reasoning. Agents such as
LLM for Scientific Visualization. While some existing works have preliminarily explored the potential of FMs in supporting visual scaffolding (Podo et al., 2024; Chen et al., 2024c; Pandey & Ottley, 2025; Hong et al., 2025), they are typically fragmented, lack pedagogical grounding, and fail to generalize across diverse educational tasks (Wang et al., 2023a; Ku et al., 2025). For instance, Visual Sketchpad (Hu al., 2024) attempts to illustrate problem-solvinn generated from code. However, these visuals are often low in quality, lack logical coherence, and fall short in explanatory depth (Wang et al., 2025). Other approaches like MatplotAgent (Yang et al., 2024), PlotGen (Goswami al., 2025), and OmniSVG (Yang et al., 2025) plotting and SVG tools to produce more accurate, data droundgr visualizations. Still, these methods are limited in scope, often addressing only isolated steps rather than providing systematic, end-to-end visual explanations of multi-step problem-solving tasks (Vázquez, 2024; Chen et al., 2024a; 2025b). Our framework, in contrast, is designed to manage the entire pedagogical workflow, from problem deconstruction to the final interactive explanation. To overcome these limitations, we propose a multi-agent collaborative framework, Edu VisAgent, that simulates the full learning journey-from initial problem exposure to deep conceptual understanding by coordinating specialized agents to generate coherent, pedagogically aligned visualizations throughout the reasoning process.
processes with sketches
LLM-based Education Agents. Recent advancements in -ased agents have led to the de- velopment of specialized architectures capable of long-horizon planning, tool use, and memory management across range of real-world domains (Yao et al., 2023; Chan et al., 2024; Chen et al., 2024b; 2025a; Nie et al., 2025; Han et al., 2025; Zhou et al., 2025). In the educational domain, AI agents such EduAgent (Xu et al., 2024) and Teachtune (Jin et al., 2025) simulate student-teacher dialogues, collaborative learning activities, and task-oriented reasoning to enhance problem-sol vooiigg instruction. Agents like SEFL (Zhang al., 2025) and PROF (Nair et al., 2024) generate on-demand feedback for large-scale educational settings, while domain-speciin tools such as MathChat (Wu et al., 2023), NEWTON (Wang al., 2023b), and MEDCO (Wei al., 2024) provide textual explanations for scientific and medical learning. Despite these advances, limited research has investigated collabo- rative, multi-agent approaches tailored to educational reasoning and visualization. Edu VisAgent is the first multi-agent framework that coordinates specialized agents to support step-by-ster pedagogical problem-solving with integrated visual explanations.
CONCLUSION
This paper addressed the challenge of generating pedagogically meaningful visual explanations with AI systems. We introduced Edu VisBench, benchmark revealing that existing models often produce inadequate visual outputs. This work provides quantitative baseline for the field, clearly identifying the key areas where current technologies fall short. To overcome this, we proposed Edu VisAgentt, collaborative multi-agen framework. Experiments show Edu EduAAsseent significantly outperforms all baselines, demonstrating the potential of agent-based systems for advancing educational visualization.

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ETHICS STATEMENT
The primary goal of this research is to advance educational technology by improving the pedagog- ical quality Al-generated visualizations, aiming for a positive societal impact. The benchmark developed, Edu VisBench, is curated from publicly available and high-quality educational resources, including WeHHChem--ennhhieg
set-sshool-physiiie Illustrative Mathematics, and MATH-500. To validate our automated evaluation metric, conducted comparative study involving human evaluators, who were undergraduate students from top universities. All data used the study was handled with
ensure anonymity and was used solely for the purpose of validating the scoring system. The models and methods proposed are intended for beneficial educational applications. The authors are not aware of any other ethical issues and declare no competing interests conflicts of interest associated with this research.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have provided detailed descriptions of our methodology and resources. The curation process for our benchmark, EduVisBench, is detailed in Section 2.2. with data sources explicitly cited. Our comprehensive evaluation framework, including the five key dimensions and scoring protocol, is described in Section 2.3 and Section 4.1eThe detailed scoring rubrics and the exact prompt used for our GPT-4o-based evaluation are available in Appendix A.2 and Appendix A.3, respectively. The architecture of our proposed Edu Vis and the roles of each specialized agent are thoroughly explained in Section 3. A complete list of all baseline models and their versions used in our experiments is provided in Section 4.1.
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A APPENDIX
A.1
VISUALIZATION DISCIPLINES
Table 4 illustrates the disciplines and types our Edu VisBench.
Discipline
Common Visualization Types
Number lines, function graphs, and other formalized visual tools.
Mathematics
Diagrams involving levers, rigid body motion, forces, and fields.
Physics
Molecular structures and schematic representations of standard laboratory apparatus.
Chemistry
Table 4: Representative Visualization Types Across Academic Disciplines
A.2 EVALUATION METRIC
Visual Scenario Design Guidance The category of "Visual Scenario Design Guidance" outlines different levels of visualizing mathematical concepts, progressing from basic text-only representations to highly integrated visual-text formats. Through five defined levels, the framework demonstrates how visual elements can enhance students' understanding and engagement with abstract ideas, guiding instructional designers to gradually enrich scenarios, add annotations, and strengthen contextual connections ultimately achieving the goal of visually presenting the full flow and conceptual structure of the content. The five levels of Visual Scenario Design Guidance are follows:
Level
Description
The image contains no scenes or illustrations, presenting only text formulas. It lacks contextual visual cues, failing to spark interest or connect the concepts to real-litt life situations.
Level
The image includes single static illustration or low-fidelity mockup with minimal labeling that does not highlight variables key objects, offering limited context and poor immersion.
Level 2
Multiple static schematic diagrams sketch-style illustrations appear in the image, labeling core objects, variables, and simple steps, providing basic visual guidance but lacking layered coherence.
Level
The image integrates scenario illustrations, storyboard panels, and info- graphics to present the process in multiple views and steps, with annotations and captions guiding students through mapping abstract concepts context.
Level 4
Storyboard-style illustrations and infographics are fused into single image, including overview, detailed close-ups, and key pathway diagrams with comprehensive annotations, allowing students to grasp the entire flow conceptual network at glance.
Level 5
Table Five Levels of Visual Scenario Design Guidance
Visual Illustration Design The category of "Visual Illustration Design" describes progressive levels of visual elements used to support students' systematic understanding of quantities and relationships. It ranges from visual aids complex integrated dashboards that deeply connect data and model
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structures. Through five levels, the framework guides designers improve clarity, coherence, and contextual richness of visual illustrations, enhancing students' analytic and comparative abilities.
Level
Deseription
The image contains no charts, axes, or flow diagrams onlll text. Without embedded visual tools, students cannot systematically organize analyze quantities relationships.
Level
The image presents static number line and colored bar chart with plete scales and legend, helping students gain basic understanding numerical changes. However, it lacks comparison and contextual lay layering.
Level
The image presents a static number line and colored bar chart with complete scales and legends, helping students grasp basic numerical changes visually, though comparison and context layering absent.
Level 3
The image combines number lines, flowcharts, infographies, and arrow annotations; multiple visuals are juxtaposed overlaid show processes variable changes for coherent modeling view.
Level 4
The image presents dashboard-style visualization integrating axes, bar charts, flow diagrams, heatmaps, etc., with linked elements that deeply visualize data relationships and model structure.
Level 5
Table 6: Five Levels of Visual Illustration Design
Text-Illustration Coordination The category of "Text- Coordination" describes levels of alignment and integration between textual content and visual elements within images. This progression ranges from complete disconnection to seamless fusion, enabling students to effectively map and synthesize text, formulas, and graphics. The framework guides designers strengthening links between verbal and visual information enhance comprehension and structural understanding.
Level
Description
Text illustrations image are completely disconnected, with no labels, legends, or connectors students cannot use visuals to understand text or formulas.
Level
Text occasionally prompts "see diagram" or "refer to the illustration." but the image lacks legends or clear labels, mapping between text and graphics remains ambiguous.
Level 2
Text descriptions and image elements share consistent numbering, color blocks, or arrows linked to simple legend, explaining core symbols and variables to support initial mapping.
Level
Text paragraphs are laid out alongside corresponding visuals within the same image, with dctailed legends and color-coded annotations enabling simultancous reading and mapping.
Level 4
Text, formulas, and legends fully integrated in image, using consis- tent colors, numbering, and layered layout achieve seamless text-graphic fusion for complete structural understanding.
Level 5
Table 7: Five Levels of Text- Coordination
Learning Thought Guidance The category "Learning Thought Guidance" describes the progres- sive inclusion of visualized problem-solving strategies and reflective cues in images. From presenting
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only problem statements to complex integrated dashboards, this framework guides designers to scaf- fold students' strategic thinking and metacognitive reflection through visual tools, enabling deeper reasoning and transfer of learning.
Level
Description
The image offers visualized problem-solving guidance, showing only problem statement formulas, leaving students without strategic cues or reflection prompts.
Level
image emheds a simple flowchart or two title-style hints (e.g., "Identify prohlem type," "Check result"), ) but the flowchart is overly simplistic and hints lack hierarchical detail.
Level 2
The image displays step-by-step flowchart template with key thinking nodes and self-check checkpoints, leaving annotation space for students visually record their reasoning.
Level
The image combines near-transfer exereise with comparative thought diagram, visually highlighting strategy differences so students can apply existing reasoning to a new context.
Level 4
The image fuses near- and far-transfer exercises, concept mind maps, and a reflection panel into dashboard-siyle layout, allowing students to review and extend their gobblem-sllingg network visually.
Level 5
Table 8: Five Levels of Learning Thought Guidance
Interactivity and Personalized Support The category of "Interactivity and Personalized Support" outlines levels of incorporating feedback, hints, and tailored assistance into images, evolving from static presentations to dynamic, student-responsive visual supports. This framework encourages designers embed interactive elements that adapt to learner needs, promoting engagement and personalized problem-solving.
Level
Description
The image includes no feedback or support components only static problem statement and answer field offering hints, examples, or error cues and resulting in nonresponsive visual.
Level
The image shows fixed hint boxes (e.g.. "Hint: draw numher line," "Hint: check rounding") ) hints are not tailored to student responses, limiting personalized guidance.
Level
The image integrates multiple static correction tips and example solution modules (common mistakes and standard approaches). which students can reference visually but without intelligent recommendations.
Level 3
The image presents example solution workflows, text hints, and common- emrors analysis section highlighted with color blocks and arows, providing diverse visual support single layout.
Level 4
The image displays a comprehensive visual support panel difficulty suggestions, personalized hints, worked examples, and extension resource links, enabling students to select tailored guidance directly from the visual layout.
Level 5
Table 9: Five Levels of Interactivity and Personalized Support
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A.3
EVALUATION PROMPT
The instructional web page evaluation prompt is structured as follows:
Evaluation Prompt
As professional evaluator of instructional web pages, your task to determine whether the generated web page meets expectations across five specifie categories.
Instructions:
Assign an integer score from to for each of the five categories (1-5).
== completely missing or extremely poor
5 fully meets the highest standard
Evaluation should be based solely on the specified aspect: (category). The definition of (category/ is: [description).
Do not include any explanation, justification, additional commentary. Refusing to provide a score not allowed.
Evaluation Output Format
((RATING: score,, "2": :cors, :4"os, "5":score]]
A.4 GENERATION PROMPTS FOR BASELINE MODELS
The following prompts are used to instruct baseline models generate instructional visualizations for multiple-choice questions.
HTML Webpage Generation Prompt
Please generate fully structured and styled HTML webpage for the following ( (subject] multiple-choice question, with focus clearly presenting the problem, visualizing key scientific concepts, and explaining the reasoning behind the correct choice.
The page should include:
A prominently displayed question section.
clearly formatted list answer choices (A, B, D).
A step-by-step explanation section that helps users understand why the correct answer right, and why the others are This section may include:
Diagrams illustrations (e.g., molecules, environmental impact visuals),
Charts or data comparisons (e.g., particulate levels),
Flowcharts or labeled process diagrams,
Any other visual representation that supports comprehension.
A clear highlight of the final correct answer (e.g., visual cue box).
Requirements:
Use modern CSS styling (inline <style> block external).
Ensure layout is responsive and readable across devices.
Use semantic HTML structure with headings and subheadings (<h1>, <h2>, etc.).
Render scientific symbols or formulas correctly (c.g., MathJax or KaTeX).
Visualization libraries such as Chart.js, D3.js, SVG may be used enhance explana-
tions.
Question: (question)
Please output the full HTML CSS JavaScript code only, without any extra explanation or comments.
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SVG Diagram Generation Prompt
Generate a stand-alone SVG diagram that visually explains the following question as reasoning task. The SVG must include:
The full question text;
Key reasoning steps, visual annotations, or illustrations that aid in understanding or solving the problem;
If applicable, highlight the final answer or conclusion clearly.
Requirements:
Output only single <svg? </svg> block, with extra text outside it;
SVG width between 800-1200px, layout should be clean and adaptive;
Use <text> elements with readable font sizes for all text;
Use arrows, symbols, and diagrams they help communicate the solution process;
You may omit multiple-choice options not relevant.
Question: {question]
Visualization Agent Generation Prompt
Your task to generate a complete webpage solution for the following problem. The page should include:
Introduce a scenario engage students.
Explain the problem background and provide a step-by-step walkthrough of the solution.
Give another similar problem assess students' understanding. Question: (question)
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A.5
MULTI-AGENT SYSTEM PROMPTS
A.5.1
TASK PLANNING AGENT PROMPT
System Prompt
Transform learner's question into structured instructional task.
Scenario & Understanding: Real-world context givens/goall/connsrains
2. Solution Strategy: Analysis + step-by-step solution + reasoning
3. Transfer Tasks:
Near-transfer: same structure, minor changes
Far-transfer: different surface, same logic
4. FOPS Structure (per step):
F: Problem type
O: Diagram/cquation structure
P: Solution path S: Execute +
verify
Specify: goal, action, concepts, pitfalls
5. UI Layout: Shaden/UI structure sections meta- meta-prompt placement math rendering
JSON Output Format
"scenario": { "context": "..." "givens": ["g!", "g2"I, "goals": ['goal1"], "comstraints": ["c1"] ),
solution_strateeyy: { "analysis": "...",
"staps": [[ "step_nunber":
"fops_label"; "F/0/P/S", "description": "What to do", "reasoning": "Why" }]
"transfer_ tasks":
"near "near_transfen transfer": : "problen": "...". "explanation": ".... },
= ( ("problen":
"explanation":
"instucciioal__seps [[ "step_number"s 1, "goal": "..."
"action": "...", "concepts": ['c1", "c2'l, "pitfalls": ['al"] )},
Lalyooussuggestion".. "structure"; "Page flow", roe_.components". ["Card", "Accordion "Alert", "Alert","Table"] "Table"), coottett_organzzatoon "...", renth__eedering" "KaTeX/MathTax"
User Prompt
Give your analysis the following question in JSON format.
Questionn]
A.5.2
CONCEPTUAL MAPPING AGENT PROMPT
System Prompt
Map concepts using CRA framework (Concrete- Reprrteeerpeeettttinll--Aaa-e Abstract).
1.
Concrete:
Objects, quantities, situations (directly experienced)
Representational Visual Design:
Tools:
number lines, bar graphs, diagrams, organizers, sketches, tables
Visual design: form, purpose, concrete-sabstrat bridge; avoid decorative
3. Abstract: Formulas, principles, theorems connection to concrete
4. Think Aloud: Verbalization prompts
JSON Output Format
{
"cra coranngnnng I[ "step_number" 1, "step_name": "..." "concrete": { ("entities": ["obj1', "aty1"], "description": "..."
"reareeetaaionall
"tsol"; "Nunber line/etc", "purpase": "Why it helps", "represents": "What it shows", "iisual_desinn" ( "form": "..." "elenents": ["... "],
"bridge": "How it bridges concrete to abstract" } }. "abstract": "constructs"": L"fornulal" "principlel"), "connection": "How abstract connects to concrete" "chink_aloud"; ['prompt1", "prompt2"] )]
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User Prompt
According to the following planning, give your analysis in JSON format. JSON]
(Task Planning
A.5.3 REASONING DECOMPOSITION AGENT PROMPT
System Prompt
Decompose reasoning using design scaffolded practice.
1. Step Guidance: Action (what learner does) + link to CRA concepts + visual support (which CRA visual interaction)
2. Practice Activities: Types (fill-in, choice, judgment, explanation) gradual release strategy
3. Math: LaTeX $... $$...$$
JSON Output Format
{ "fops_ reasoning:: [[ "action": "What learner does", "concepts": "c2"J, "scaffolding
true, "Why and how scaffold", visual_support" { ("neaded": true,
"scaffolding_non
"which_visual": "CRA napping ref", "interaction": "...") }l.
"practice_cctivttiee activities": Uf aetivity_nunber" 1,
"name": "Activity name", "type": hnll-iiddoooerjjiigmmmeeaaaaaat
"task": "Problen or task content',
relasse" "How independence builds" }]
User Prompt
According to the following planning and conceptual mapping, give your analysis in JSON
format.
(Task Planning JSON) (Conceptual Mapping JSON]
A.5.4 METACOGNITIVE REVIEWER PROMPT
System Prompt
Generate metacognitive prompts for monitor, evaluate, regulate.
Reflective Prompts: Check understanding, comprehension, strategy
2. Self-Questioning: "What did I do?" / "Why does this work?" / "What did miss?" / "How does this connect?" "What if..?"
3. Self-Correction: Checkpoints + error identification
4. Organize by Phase: Before (understanding planning) During (monitoring + checking)
+ After (evaluating reflecting)
Math: LaTeX $...S $$...$$
JSON Output Format
mntaccognitvve iivi_ottosss"
seefore_solving". ['Understanding check 1", "Planning prompt 1'],
"during. solving": ("Step monitoring 1", "strategy adjustment 1"],
"after_solving L"Verification 1", "Reflection 1", "Inprovenent 1*]
"strategy": "Self-monitoring technique". when_ta__se" "During which phase",
"hou_to_apply": "Specific actions"
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User Prompt
According to the following planning, conceptual mapping and reasoning decomposition, give your analysis in JSON format.
(Task Planning !
(Conceptual Mapping JSON] (Reasoning Decomposition JSON}
A.5.5 VISUALIZATION AGENT PROMPT
System Prompt
As the Visual Representation Specialist, generate the visualization teaching webpage of the given question.
1. Abstract Visualization Tools: Encourage number lines, bar graphs, object diagrams, organizers, sketches, and data tables.
2. Avoid Decorative Imagery: No photorealistic or cartoon-style images; visuals must remain schematic and pedagogical.
3. Conceptual Mapping: Explicitly explain how each visual corresponds the underlying abstract concept (e.g., number line for quantity change).
4. UI Integration: Specify how visuals should appear in the web UI (placement, sequencing, interaction).
5. Rendering: Math rendered with KaTeX/MathJaa.
User Prompt
Using all of the given data, generate visualized teaching webpage for the following question. [Question) (Task Planning JSON] (Conceptual Mapping JSON)
[Reasoning Decomposition JSON]
[Metacognitive Reviewer JSON)
B ADDITIONAL RESULTS
B. ABLATION STUDIES ON EDUVISAGENT
We conduct ablation studies the multi-agent teaching system in Table 10.
We compare the overall Total score of the complete system (Full) against six variants: -TP (without Task Planning), -CM (without Conceptual Mapping), -RD (without Reasoning Decomposition), -MR (without Metacognitive Review), -VIS (Using Claude 3.7 Sonnet as the underlying model while keeping the same multi-agent pipeline), and Single (a single-agent baseline that uses the same of prompts without explicit modularization or inter-agent coordination).
Impact of Removing Individual Modules. The ablation results reveal clear and systematic evi- dence that each module in the multi-agent architecture contributes a distinct cogmitive function whose removal leads to measurable degradation. Across all subjects and difficulty levels, dropping any single module results in a consistent decline of approximately 4 6-6 points relative to the full model, indicating that no component is redundant.
Task Planning (-TP). Removing the Task Planning module reduces the system's 's ability to structure the solution trajectory at the outset. This manifests in weaker performance particularly on Medium and Hard problems, where multi-step planning essential. Without this stage, the problem-solving process becomes more linear and less globally coherent, leading to incremental reasoning errors that accumulate throughout the solution.
Conceptual Mapping (-CM). The Conceptual Mapping module provides the system with an in- termediate representational scaffold way to organize domain concepts, formulas, and symbolic relations before detailed reasoning begins. Its removal produces one the largest degradations across domains, showing that the system heavily relies this symbolic "schema construction" phase.
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Table 10: Ablation study on EduVisAgent. Full denotes our complete multi-agent system. Single denotes single-agent baseline using all prompts without modularization. The highest scores are in bold.
Subject
Difficulty
Full
-TP
-CM
-RD
-MR
-VIS
Single
Easy
69.00
64.60
63.80
63.00
64.20
64.80
59.30
Chemistry
Medium
76.27
71.87
71.07
70.27
71.47
72.07
67.42
Hard
76.00
71.60
70.80
70.00
71.20
71.80
66.85
Easy
85.33
80.94
80.14
79.34
80.54
81.14
76.10
Physies
Medium
81.71
77.31
76.51
75.71
76.91
77.51
72.05
Hard
84.00
79.60
78.80
78.00
79.20
79.80
74.32
Easy
90.20
85,80
85.00
84,20
85,40
86.00
81.05
Maths
Medium
64.50
60.10
59.30
58.50
59.70
60.30
55.27
Hard
65.00
60.60
59.80
59.00
60.20
60.80
53.88
Without CM, the reasoning tends to jump directly into procedural steps without establishing the underlying conceptual structure, which leads to misapplied rules, inconsistent variable usage, and missing constraints.
Reasoning Decomposition (-RD). This module creates explicit fine ine-grained segmentation of logical steps, and its absence consistently yields the largest drop among all ablations. Without RD, the model tends to condense multi-step reasoning into single large leaps, increasing the likelihood of hidden errors that are never surfaced or corrected. RD therefore serves as the backbone of reliable multi-hop reasoning, ensuring that intermediate steps are interpretable, verifiable, and less prone compounding mistakes.
Metacognitive Review (-MR). The MR module as internal critic, performing self-evaluation and error checking. Removing MR reduces the model's ability to detect calculation inconsistencies, missing assumptions, or logical conflicts within its own output. While the primary reasoning path remains intact, the absence of this reflective layer leads to small but systematic accuracy losses, especially on tasks requiring unit checking, boundary conditions, or verification of derived results.
Model Replacement (-VIS). Replacing the underlying model with Claude 3.7 Sonnet while preserving the multi-agent structure produces moderate drop across categories. The decline is smaller than removing core cognitive modules, demonstrating that the multi-agent pipeline itself contributes substantial performance stability. However, also indicates that the pipeline and the underlying model are jointly responsible for peak performance.
Single-Agent Baseline. The Single baseline, which collapses all prompts into a single monolithic agent, performs the worst across all conditions. Its scores are consistently below every multi-agent ablation, averaging 8-15 points lower than the full system. This sharp decline highlights that the benefits of the multi-agent design arise not merely from the prompts themselves, but from the explicit architectural separation of planning, conceptual structuring, stepwise reasoning, and self-review.
B.2 EVALUATION ON NON-STEM DATASETS
Table Performance of Visualization Agents on Edu VisBench.
Method
Vis. Type Prehistory Sociology
Avg
vθ
Wehpage
61.8
68.0
64.9
Edu VisAgent
Wehpage
79.4
84.8
82.1
To further explore Edu VisAgent's performance non-STEM disciplines, we selected 100 problems from MMLU's prehistory and sociology categories to evaluate Edu Vdigen''ssn''s adaptability narrative and conceptual reasoning tasks. Results in Table 11 show that Edu VisAgent significantly outperforms
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the current SOTA baseline (v0) by 17.2 points, demonstrating that multi- agent framework effectively generalizes to humanities education.
B.3 EVALUATION ROBUSTNESS
We tested two other evaluators, Gemini 2.5 pro and Claude 3.7 Sonnet to score visualization results of SDXL, GPT-40(SVG) and EduVisAgent The results in Table 12 show consistent scoring patterns across different evaluator models. While there are minor numerical variations in the absolute scores, the overall trends and relative rankings remain highly consistent with the GPT-4o results.
Table 12: Evaluated by GPT-4o, Gemini 2.5 Pro and Claude 3.7 Sonnet on Edu VisBench.
Method
Vis. Type
GPT-4o
Gemini 2.5 Pro
Claude 3.7 Sonnet
SDXL.
Image
21.8
18.9
24.7
GPT-4o
SVG
25.4
26.3
27.4 81.6
Edu VisAgent
Wehpage
78.9
84.4
C ADDITIONAL MATHEMATICS EXAMPLES IN EDUVISBENCH
Figure 8a, 8b, 8c and 8d presents four representative problems from the Hard subset (MATH-500) in Edu VisBench, illustrating the range and difficulty the mathematical reasoning challenges in the benchmark.
Nathomaltee
Mathoraatos
Wa de lurpust inlas of with the groph dry searnce shiry
Score
(a) Representative example
(b) Representative example 2
Les 2- 2 - Che--iheie Di and be to It
Carasaa De Baciic
mosic
(c) Representative example 3
(d) Representative example
Figure 8: Representative examples from the Hard subset (MATH-500) of Edu VisBench.
D REAL-WORLD DEPLOYMENT OPTIMIZATION
For real-world deployment, we identify several optimization directions: (1) reducing token consump- tion through more efficient prompt engineering. (2) optimizing the metacognitive reasoning depth to balance quality and efficiency, and (3) implementing caching mechanisms for frequently used educational concepts. Additionally, the system could operate asynchronously. Teachers prepare materials in advance rather than real-time generation, making latency less critical.
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E FULL PROMPT AND INTERMEDIATE STRUCTURES FOR THE ANGULAR VELOCITY EXAMPLE
We present the complete prompt and intermediate output structures for all five agents in the angular velocity example.
Agent 1: Task Planning
task_plae planning::
scenario:
context: A wheel rotating with a given angular acceleration and initial angular velocity. the task to determine angular velocity after specific time.
givens:
Angular acceleration (Nalpha)
Initial angular velocity (\onega_8) rad/s
Time (t) seconds
goals: [Find the angular velueity (Norega) after 5 seconds]
constraints: [Use the kinematic equation for angular motion] solution.stratey.
analysis: This problen invelves angular notion with constant angular acceleration. angular velocity
calculated using Vonega \onega_0 Valpha
steps:
step_nunber:
faps_label: Constant acceleration O: Vanega \onega_ Valpha Substitute Solve
description: Use Vonega \ongaa_8 8 + \alpha t. Substitute the given values.
reasoning: This
relates all quantities. Directly applicable.
$tep_nunber: 2
faps_ Substitution Vonega Nonega_@ Valpha description: Substitute vonega_8=2, _22 Valphaw4, t-5 into Yonega 2 (4 5).
Arithmetic Verify
reasoning: Substituting the values allows direct calculation.
step_nunber:
labe_ F: Final calculation / 0: \omega 2+20 / P: Simplify / S: Verify
description: Simpliif: Vonega
reasoning: Ensures the final value is correct and natches given options.
transfer_ transfer_tasks: tasks:
near_tran _ra__tranefe::
problen: wheel rotates with angular acceleration rad/s*2. IF its initial
angular velocity is 5 rad/s, what is its angular velocity after 4 seconds?
explenation:
original with different
far far_transfer:
velocity explanation: Involves linear motion instead of angular, but sane kinematic logic.
problen: accelerates unifornly rest m/st2,
instructional.steps
step_nunber:
goal: Understard the problem and identify given values.
action: Extract Yalpha, Vonega_0,
problem concepts: [Angular notion, Kinenatic equations]
pitfalls: [Confusing angular linear velocity, Misidentifying given values]
step_number:
goal: Apply the kinenatic equation for angular velocity.
action: Write Vonega \omega_@ Valpha and substitute the given values.
concepts: [Kinematic equation for angular notion]
pitfalls: [Incorrect substitution, Forgetting initial angular velocity]
step_number:
goal: Calculate the angular velocity.
action: Perforn the arithmetic calculation to f'ind Vonega.
concepts: [Basic arithmetic, Angular velocity]
pitfalls: [Arithnetic errors, Misinterpreting the result] ui ui_Layout_ suggestion:
sections scenario, solution, practice. key_corponents: [Card, Accordion, Alert, Table]
structure: Step-by-step instructional layout
content_organt organizationn.. Scenario at top, solution strategy steg-by-step, practice at end. math_rendering Use KaTex MathJax for equations like Vonega Vonega_8 Aalpha
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Agent 2: Conceptual Mapping
conceptual_ mnccpptull_mappinn:
cra_nagping:
step_number:
step_nane: Understand the problen and Identify the given values
concrete:
entities: [wheel, angular acceleration, initial angular velucity, time] description: A rotating wheel with given angular acceleration and initial specific time.
angular velocity
representational:
tool: Table
Organize the given values clearly for reference.
represents: Yalpha, Nonega_e, and visual_design:
farm: Simple table with colums for quantity, synbol, and volue.
muantity/syybal// /value, bridge: Transition from problem statement to identifying needed variables.
[Headers
Valpha/Vonega_0//
abstract:
Valpha t] cornection; Relates given quantities (Nalpha, \onega_ 8, t) to unknown Vonego.
constructs: [Kinenatic equation: Aonega \onega_0
think_aloud: [What quantities given?, Mhat the goal?]
step_number:
step_näne: Apply the kinematic equation for angular velocity
concrete:
entities: [angular velocity, kinenatic equatien]
description: Using Vorega Yomega_ \alpha to calculate angular velacity.
representational:
tool:
Equation
purpose: relationship
Vonega, Yonega_8 Nalpha,
represents: The kinenatic equation for angular notion.
[isual_design::
farm: Standard nathenstical notation. elements: [onega = \onega_8 \alpha t]
bridge: Links abstract formula concrete problen substitution.
tool: Dynanic animation (aptional)
purpose: Illustrate Vanega changes time constant acceleration.
represents: Growth of angular velocity as time progresses. Disual_design
farm: Graph showing Aonega increasing linearly with time.
elements: [Time on x-axis, Angular velocity on y-axis]
bridge: Visualizes relationship described by the equation.
abstract:
constructs: [Substitution values \onega \omega_@ Valpha
connection: substituting concrete values into abstract fornula.
think_aloud: Dihat equation to use?, How substitute values?]
step_number:
step_nare: Calculate final angular velocity
concrete:
entities: [arithnetic calculation, final angular velocity]
description: Performing the calculation to find the final angular velocity. representationsl:
tool: step arithnetic
purpose: Break down calculation into smaller steps for clarity. Vonega
represents: process sisual_design:
Sequential steps showing part calculation.
elements: (Substitute values, Multiply \alpha and Add Vonega_0]
bridge: Connects abstract substitution to concrete result. tool: Alert Feedback box
purpose: Highlight comnon errors in arithnetic or substitution.
represents: Potential pitfalls
calculation process.
visual_desiag design:
form:
tips or warnings.
elenents: [Error examples, Corrective suggestions]
bridge: Prevents errors and reinforces correct methods.
abstract:
constructs: [Final result: Vonega = 22 rad/s]
realization of formula. tlouk_ [What is the next step?, Does the result make sense?]
connection: calculated result
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Agent 3: Reasoning Decomposition
reasoning denpriigggdccoossittioo..
Fops_reasoning:
Extract Valpha, Vonega_0, from problem statement. concepts: [Angular motion, Kinematic equations]
action:
needeed: true
scaffolding_nates: Learners my canfuse angular with linear velocity misidentify values. Provide table to organize.
visual support:
needed: true
wisch_ Table organizing given values
interaction: Draggable labels to natch quantities with synbols and values action: Write lonega NonegaL8 \alpha and substi tute the given values. [Kinematic equation for angular astion]
concepts:
scuffolding true
scaffolding Learners may struggle identifying correct equation
substitucing correctly. Provide guided substitution exercise.
visual support:
needed: true
which_visual: Equation with placeholders for substitution
interaction: Reveals for substitution step action: Perform the arithmetic calculation to find Yonego.
concepts: [Basic arithnetic, Angular velocity]
lelfoollnnnneeddeee true
scaf nolefiiingnnnees: Arithmetic errors are connen. Break down calculation into smaller steps provide feedback
visual_support
needed: Step-by-step arithnetic breakdow
which_visual;
interaction: Dynamic reveals each calculation step
practice sereitseeactvvitiiss
activity_nunber:
name: Identify Given Values type: fill-in
Fill table values Valpha, \onega_0, gradual_release: Guided examples => independent identification.
activity_nunber:
name: Substitute Values inta Equation type: choice
task: Choose correct substitution for Yanega \onega Yalpha t given \alpha=4, \omega_0=2, t=5,
gradual_release: Hints and feedback + independent substitution. activity_nunter
name: Calculate Angular Velocity | type: judgrent
task: Perforn Vonega
5) verify correctness.
gradual_release: step-by-step guidance -> independent calculation with feedback. activity_nunber::
Near Transfar Problan type: explanation
task: wheel with Yalpha=3 rad/s"2, tonega_0=5 rad/s. Find Yonega after 4s. [eduul__eleese: Guided steps independent problem-solving.
activity_nunter 5
type: explanation task: car accelerates from rest m/s-2. Velocity after seconds?
nane: Transfer
gradual_releasee Comparison to angular notion >> independent solving.
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Agent 4: Metacognitive Reviewer
netacognitive_ro revievee:::
vetacognitive_prrame
before_sso solving.
Mhat quantities are given in the problem, and what do they represent?
Mhat is the goal of the problen, and how can achieve it?
surinngsollvin:
An using the correct equation for this problen?
carrectly
equatien?
after_solving:
Does nly result make sense given the context of the problen?
Mhat could improve in appreach to solving similar problens?
general_strategies:
strategy: Self-menitoring technique
when_to__see During substitution calculation phases
how_to.apply:
Pause after each step to verify the accuracy of substitutions and celculations.
strategy: Error-checking routine
when_to_use: After completing the calsulation
how_to_apply:
Revisit the problen statement and verify that the result aligns with the given values and context.
strategy: Visualization
when_to_use: During the understanding phase
how_to_ how_to_apply:
Organize the values table diagran clarify relationships.
Agent Visualization Page Design
visual iounlization_aage:
lovovt_overvvew:
structure: step-by-step instructional layout with clearly separated sections.
sections:
(id: ccenario.overview, title: Scenario Overview, content: Problen statement given values)
(id: stt..yyyttspsouttin title: Step-by-Step Solution, content: Interactive visuals)
{id: practice_preblens, title: Practice Problens, content: terrffa-trrasffee tasks)
(id: prrlection_prompts. tiele: Reflection Prompts, content: Metarogmitive questions)
ui_components: [tards, Accordion, Alerts, Interactive visuals ttaalles//euttttoonammmttnnnn
static_visuals:
step_1: (goal: Organize given values, type: table, columns: [Quantity, Synbol, Valuel,
rows: [Nalpha, lomega_0, tl, interaction: Drag-and-drop labels)
step_2: (geal: Hake equation explicit, equation: Vonega \onega_e \alpha t,
substitution: Vanega 2+(4x5), interaction: Click placehalders reveal values)
step_3: (goal: Transparent arithmetic, breakdown: [4x5=20, 2428=22], form: flowchart) bynanic_behavior::
angular_velocity.
purpose: Show Vonega increasing linearly constant \alpha.
axes: Time 8-5s, Yonega 8-22 rad/s)
behavior: (start: 2, end: red/s), interaction: Aninate on load.
suppisse_ubstitution
purpose: Visualize substitution in Vonega = \anega_8 Nalpha t, placeholders: ('omega_B=2, Valpha=4, t=5], timing: fade-in per reveal. user per__iteraccionss
interactive table:: Drag-and-drop natch quantities, symbols, values. equation, substitution: Click placeholders reveal substituted values. breaktoon::: "Next step" revealing 4x5=20, then 2+28=22.
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F
QUALITATIVE SUCCESS AND FAILURE CASES
SUCCESS CASES
Talto
Pikae
Mutert
Mos bew
Understarding the Contect
manahh ahocee
Figure 9: Success case 1 illustrating coherent multi- agent collaboration.
Figure 10: Success 2 illustrating coherent multi- agent collaboration.
Neatly
Vimal
Figure 11: Success case illustrating coherent multi- agent collaboration.
Figure 12: Success case illustrating coherent multi- agent collaboration.
Figure 13:
Success case 5.
Figure 14: Success case
Figure 15: Success case 7.
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FAILURE CASE: ERROR ACCUMULATION IN THE BULTI-AGENT PIPELINE
We illustrate a representative failure mode in which an early planning error propagates through the multi-agent pipeline. In Step 1, the task_planning agent proposes inappropriate ui werssssuuggsssinniiieee ingful relation the underlying concept of angular velocity. In Step 2. downstream reasoning agents correctly infer the need for a rotating-wheel visualization, yet the erroneous layout suggestion contin- ues to impose irrelevant UI constraints. Step 3, the Visualization Agent attempts to comply with upstream directives, producing non cee--hysics-reattte related components instead of correct angular-motion visuals. By Step 4, the multi-agent chain has amplified the initial mistake: despite correct domain inference, the final UI is structurally misaligned. The final outcome (Step 5) shows that accumulated constraint errors prevent the system from producing any meaningful visualization related to angular velocity, demonstrating error propagation in multi-agent settings.
such as Card, Accordion, Alert, and Table bear no mean-
Expressing
Presken
Pe anoua acce reder PILL reDo SOOR Vnac Du
Tyrtel
Figure 16: Failure case step inappropriate layout suggestion.
Figure Failure case step erroneous constraints propagate.
Figure 18: Failure case step 3: non- Figure 19: Failure case step 4: struc- turally misaligned UI.
Figure 20: Failure case step 5: no meaningful visualization.
physics components.
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Reviews:
[{"review_id": "Zv2AWnOG9h", "reviewer": "Reviewer_37gW", "summary": "This paper addresses the critical and underexplored challenge of generating pedagogically effective visual explanations using foundation models. The authors argue that existing models, while proficient in textual reasoning, fail to create structured, interpretable visualizations that support conceptual understanding in educational contexts.\nTo address this, the paper presents two primary contributions:\n\n1. EduVisBench: A comprehensive benchmark for evaluating the pedagogical visualization capabilities of FMs. It consists of 1,154 STEM problems across Mathematics, Physics, and Chemistry, organized by difficulty. Crucially, it introduces a fine-grained, five-dimensional evaluation rubric  grounded in pedagogical principles.\n\n2. EduVisAgent: A novel multi-agent collaborative framework designed to excel at this task. Inspired by expert instructional design, the framework coordinates five specialized agents to systematically decompose a problem, structure the reasoning process, and generate a coherent, interactive, and visually grounded solution.\n\nThrough extensive experiments on EduVisBench, the authors demonstrate that existing state-of-the-art FMs and LVLMs perform poorly. In contrast, their proposed EduVisAgent achieves an average score of 81.6%, representing a substantial 40.2% relative improvement over the best-performing baseline, validating the effectiveness of their structured, multi-agent approach.", "questions": "1. Could you provide more insight into the necessity of each of the five agents?\n\n2. How do you envision the EduVisAgent framework being adapted for educational domains outside of STEM that rely on more narrative or conceptual reasoning?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper tackles a timely and important problem. As AI becomes more integrated into education, the ability to generate not just correct answers but effective teaching materials is paramount. The focus on pedagogical visualization as a distinct capability gap in FMs is novel and well-motivated.\n\n2. The design of EduVisAgent is not an ad-hoc collection of agents but is thoughtfully grounded in pedagogical theory, mimicking the division of labor in instructional design. The performance improvement is not marginal; a 40.2% relative gain over the strongest baseline is substantial.", "weaknesses": "1. The EduVisAgent framework consists of five distinct agents. While the overall system is highly effective, the paper lacks an ablation study to analyze the individual contribution of each agent. For example, how critical is the Metacognitive Reviewer or the Conceptual Mapping Agent to the final score? Understanding the impact of each component would provide deeper insight into the architecture and help identify the most critical elements for pedagogical visualization.\n\n2. The benchmark and agent are designed for STEM problems that typically have a clear, decomposable reasoning path. It is unclear how this framework would generalize to more qualitative or open-ended domains, such as literature, history, or social sciences, where visualization might serve to illustrate arguments, relationships, or timelines rather than step-by-step problem-solving.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HFQUzDIvK9", "reviewer": "Reviewer_3Ado", "summary": "This paper introduces EduVisBench, a multi-domain and multi-level benchmark designed to evaluate the visual reasoning capabilities of foundation models in educational contexts. The benchmark includes diverse STEM problem sets and a fine-grained evaluation rubric grounded in pedagogical theory. The authors also propose EduVisAgent, a multi-agent framework that coordinates specialized agents for instructional planning, reasoning decomposition, and visualization design.", "questions": "(1) Could the authors provide an ablation study to show the individual contribution of each agent (e.g., removing the metacognitive reviewer or reasoning decomposition agent) to the overall performance?\n\n(2) While automated scoring is efficient, have the authors considered a more extensive human evaluation with actual educators or students to assess the pedagogical effectiveness of the generated visualizations?\n\n(3) What are the main practical challenges in deploying EduVisAgent in real educational settings (e.g., latency, integration with LMS, adaptability to different curricula)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "(1) The formulation of a multi-agent system specifically tailored for pedagogical visualization seems novel.\n\n(2) The paper is well-executed, with a rigorous experimental setup involving multiple model families.\n\n(3) The writing is clear and well-structured.", "weaknesses": "(1) While the use of GPT-4o as an automated judge is validated, it remains a single-model evaluator. Including more diverse evaluators (e.g., human teachers, multiple LVLMs) could strengthen the reliability of the scoring system.\n\n(2) The paper does not include an ablation study to analyze the contribution of each agent in EduVisAgent. Understanding which components are most critical would help future researchers prioritize agent design.\n\n(3) The multi-agent system is computationally intensive. A discussion of inference time, resource requirements, or potential optimizations would be useful for real-world deployment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pp2kFOTYyk", "reviewer": "Reviewer_v6KF", "summary": "- The authors propose a methodology to address the limitations of existing generative models in creating effective visual explanations.  \n- They introduce EduVisBench, a metric for evaluating the educational score of visual materials.  \n- They successfully utilize the EduVisAgent multi agent framework to generate high quality educational visualization data.  \n- EduVisAgent achieved a significant performance improvement over existing models through the collaboration of agents with instructional strategies.", "questions": "1. Are there plans to provide a public repository link for the EduVisBench dataset, and will high resolution versions of figures, such as Figure 3, be supplemented?  \n2. Can you disclose the specific generation prompts used to evaluate the baseline models in Table 1 to allow for the verification of reproducibility?  \n3. Is it correct that all agents, except the visualization agent, are composed of LLMs? If correct, are the authors willing to clearly disclose the specific details of the structured prompts used to implement the education theories within the LLM-based agents?  \n4. Are there plans to provide an additional analysis on the mechanism by which each theoretical implementation maximizes the education effectiveness of the generated output?  \n5. The baseline comparison was made against simple LLMs, is it a comparison possible against multi-agent systems or recent prompt engineering techniques?  \n6. What was the primary reason for constructing a multi-agent system? Given that many recent LLMs have significantly larger input token limits, what is the performance difference when all prompts are aggregated and input to a single large LLM versus the proposed multi-agent approach?  \n7. Although \"six specialized expert agents\" are mentioned on line 86, Section 3 EduVisAgent only has five bolded agents. What does the remaining one refer to?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- Developed EduVisBench, a benchmark with richer information compared to datasets from existing generative models.  \n  - Successfully validated the reliability of the LLM-based automatic evaluation system using human assessments shown in Table 2.  \n- Utilized five specialized multi agents to implement strategies.  \n- Tested broad generalization capabilities across three major academic domains: mathematics, physics, and chemistry.", "weaknesses": "-  A detailed explanation of the dataset utilized for evaluation is required, as the content presented in Figure 3 is unclear.  \n- The input prompts used for the LLM evaluation in Table 1 were not disclosed, making the verification of fairness difficult.  \n- The description of each EduVisAgent is too simple, leaving the method of theory implementation unclear.  \n- The description of how the theory was implemented in the system is lacking.  \nOpenness regarding the benchmark and the educational data is important for validating the reproducibility and reliability of the research.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wP9q3CK5jC", "reviewer": "Reviewer_M74H", "summary": "The paper introduces EduVisBench, a new benchmark to evaluate how well AI models generate pedagogically sound, step-by-step visual explanations, and proposes EduVisAgent, a multi-agent framework that significantly outperforms existing models by coordinating specialized agents for planning, reasoning, and visualization to create more effective and interactive learning tools. However, the paper lacks completeness in two core aspects: the composition of the benchmark and the implementation details of the multi-agent system. Please refer to the Weakness section for more details.", "questions": "see weakness", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper introduces the first benchmark for visualized instruction, which is one of its key contributions.\n2. This paper is well written, with a clear structure.", "weaknesses": "1. The benchmark samples presented are not sufficiently representative. For example, the visualization of mathematical instruction is based on Math 500, a dataset with the difficulty level of high school math competitions. However, the left panel of Figure 3 only shows a simple addition problem (7 + 9), which is not adequate or representative.\n2. The paper lacks a concrete description of the process for converting text problems into image problems, for example how prompts are designed and configured.\n3. Regarding the proposed multi-agent method, although it is stated to consist of multiple agents, the paper does not detail the prompt design for each agent or which models were used. A careful check of the appendix revealed no such information—this is a significant omission in terms of completeness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UTNgNH9VBX", "reviewer": "Reviewer_E8ZM", "summary": "This paper introduces EduVisBench, a benchmark designed to systematically evaluate the pedagogical visualization capabilities of foundation models such as diffusion models and LVLMs. The study reveals that existing models struggle with visual reasoning, semantic alignment, and text–graphic coherence. To address these issues, the authors propose EduVisAgent, a multi-agent collaborative framework comprising agents for task planning, conceptual mapping, reasoning decomposition, metacognitive review, and visualization design. Experimental results show a 40.2% improvement over state-of-the-art baselines, demonstrating superior pedagogical coherence, logical structuring, and interactivity.", "questions": "1. Are there any conflicts or redundancies among the agents? Have inter-agent dependencies been empirically analyzed or ablated?\n\n2. Has the pedagogical efficacy been validated with human teachers or learners?\n\n3. Could the GPT-4o-based evaluation introduce model familiarity bias toward similar architectures?\n\n4. Will the authors release full source code and interactive visualization generation modules?\n\n5. Can EduVisBench be extended to non-STEM disciplines or open-ended educational reasoning tasks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper identifies a genuine research gap in the pedagogical visualization ability of foundation models.\n\n2. Results across multiple STEM domains convincingly demonstrate performance gains with detailed quantitative metrics.\n\n3. The five-dimension rubric provides a reproducible and extensible evaluation standard.", "weaknesses": "1. Limited real-world validation: While grounded in educational theory, no classroom-level or human-teacher evaluation supports pedagogical impact.\n\n2. Incomplete interpretability of agent collaboration: The internal coordination among agents lacks empirical or ablation-based justification.\n\n3. Evaluation bias risk: Heavy reliance on GPT-based automated scoring may introduce bias or circular reasoning.\n\n4. Limited domain generalization: The benchmark focuses on STEM subjects; extension to other domains remains unclear.\n\n5. Density and readability: The paper is information-heavy, which may reduce accessibility for general AI researchers.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)