Shuyi Wang

Classroom Lecture Diagram Generator

Transform lecture transcripts into a complete set of Keynote-style 16:9 educational infographics, outputting two documents: an illustrated lecture version and a minimal image collection. One concept per image with fine-grained breakdown, four precision rules ensuring quality, five visual pattern templates, per-image visual QA, and programmatic verification before delivery.

Classroom Explanation Diagram Generator

Transform the lecture transcript into a complete set of Keynote-style 16:9 teaching infographics, outputting both a text and image explanation version and a minimalist image collection in two documents. Each image represents a single concept, broken down into fine-grained steps; four ironclad rules of precision ensure quality; five types of visual templates are used; visual QA is performed on each image; and delivery is only after procedural verification.

Classroom Explanation Drawing Board

Transform any content into a series of hand-drawn whiteboard-style infographics suitable for classroom presentations. These infographics break down topics into fine details, retain key features, and adapt to varying content lengths, providing instructors with ample visual material to support their explanations.

Academic Poster Generator

Transform an academic research paper PDF into a single portrait academic poster image end-to-end. AI adaptively determines visual style, information density, and color scheme, identifies the paper's "distinctive anchors" and presents them prominently, redraws diagrams, preserves original data charts, recreates evidence visuals (UI screenshots/chat screenshots) as high-fidelity mockups when extraction fails, filters out informal publication markers like preprint pagination, and generates a print-ready high-quality poster.

Cartoon-style explanation

This software uses a cartoon-style visual approach to explain complex concepts to beginners. It automatically modularizes the content, generating data visualization-style cartoon images (16:9 aspect ratio, white margins, specific color scheme) for each module. It integrates the original materials with AI-generated images, outputting both full and simplified versions of the document, suitable for teaching and demonstration scenarios.

This process transforms an academic paper PDF into a single A0-sized vertical academic poster image. AI adaptively judges visual style, information density, and color scheme, identifies and prominently displays the paper's "key anchor points," redraws schematic diagrams, preserves original data charts, and redraws evidence-related visuals (UI screenshots/dialogue screenshots) into UI mockups with "schematic diagram" labels when extraction is not possible. Simultaneously, it identifies and filters informal formatting markers such as temporary page numbers for first-release online publications. Finally, GPT Image 2 generates a high-quality poster suitable for printing and posting in a single step.