MAIC-UI: Making Interactive Courseware with Generative UI
Shangqing Tu, Yanjia Li, Keyu Chen, Sichen Zhang, Jifan Yu + 5 more
TLDR
MAIC-UI is a zero-code system that lets educators quickly create and edit interactive STEM courseware from existing materials using generative AI.
Key contributions
- Enables educators to create and edit interactive STEM courseware from textbooks, PPTs, and PDFs with zero code.
- Uses structured knowledge analysis and a two-stage pipeline for pedagogical rigor and content alignment.
- Achieves sub-10-second iteration cycles via Click-to-Locate editing and incremental generation.
- Lab study showed improved learnability; classroom deployment boosted STEM scores by 9.21 points.
Why it matters
Traditional interactive courseware creation is complex, requiring coding skills, and existing AI tools are often static or slow. MAIC-UI provides a fast, pedagogically sound, and user-friendly system, empowering educators to create dynamic content. This innovation can significantly improve STEM learning outcomes and reduce educational disparities.
Original Abstract
Creating interactive STEM courseware traditionally requires HTML/CSS/JavaScript expertise, leaving barriers for educators. While generative AI can produce HTML codes, existing tools generate static presentations rather than interactive simulations, struggle with long documents, and lack pedagogical accuracy mechanisms. Furthermore, full regeneration for modifications requires 200--600 seconds, disrupting creative flow. We present MAIC-UI, a zero-code authoring system that enables educators to create and rapidly edit interactive courseware from textbooks, PPTs, and PDFs. MAIC-UI employs: (1) structured knowledge analysis with multi-modal understanding to ensure pedagogical rigor; (2) a two-stage generate-verify-optimize pipeline separating content alignment from visual refinement; and (3) Click-to-Locate editing with Unified Diff-based incremental generation achieving sub-10-second iteration cycles. A controlled lab study with 40 participants shows MAIC-UI reduces editing iterations (4.9 vs. 7.0) and significantly improves learnability and controllability compared to direct Text-to-HTML generation. A three-month classroom deployment with 53 high school students demonstrates that MAIC-UI fosters learning agency and reduces outcome disparities -- the pilot class achieved 9.21-point gains in STEM subjects compared to -2.32 points in control classes. Our code is available at https://github.com/THU-MAIC/MAIC-UI.
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