课程即代码：STEM教育中教学设计的AI辅助架构
Curriculum as Code: An AI-Assisted Architecture for Instructional Design in STEM Education
- Universidade Federal de Sao Paulo (Unifesp)（圣保罗联邦大学）
- Institute of Technology and Leadership (Inteli)（技术与领导力学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出基于课程即代码的六阶段AI辅助教学设计架构，结合生成式AI与LaTeX、Python，经验证可降低教师STEM教学材料创作工作量，提升材料质量与可扩展性。
AI中文摘要：
贡献：本文提出一种基于“课程即代码”范式的六阶段AI辅助教学设计架构，将生成式AI与LaTeX、Python集成，用于自动化创建可复现、视觉一致且技术精准的STEM教育材料。背景：为主动学习定制教学材料给教师带来繁重工作，标准演示工具对技术内容支持不足，现有AI应用常产生幻觉且无法形式化学科教学创作过程，限制其在严谨学术设计中的效用。预期成果：该框架旨在减少备课时间，同时确保数学准确性、符合机构视觉标识，并通过明确规则保留教师隐性教学知识。应用设计：解决方案包含六阶段流程，以系统工作流替代临时提示工程，利用基于文本的界面和代码驱动生成（幻灯片采用LaTeX/Beamer，图形采用Python），受教学约束、情境校准及自动审核周期管控。发现：在基于项目的学习环境中，经一年时间8个模块、28个项目场景验证，该架构显著降低教师工作量。生成的资源经独立同行评审并由6位不同教师部署，确认其可扩展性超出单个创作者。基于600余份自愿学生评价，材料获得8.5至9.9/10的高质量评分。结果表明该架构具有高可复现性、最小化幻觉，且保持教学与视觉保真度，显示其适用于广泛STEM教育应用。
英文摘要：
Contribution: This paper presents a six-phase AI-assisted instructional design architecture based on the Curriculum as Code paradigm, integrating Generative AI with LaTeX and Python to automate the creation of reproducible, visually consistent, and technically precise materials for STEM education. Background: Creating customized instructional materials for active learning imposes a heavy workload on faculty. Standard presentation tools lack robust support for technical content, while current AI applications often hallucinate and fail to formalize the instructional authoring process, limiting their utility for rigorous academic design. Intended Outcomes: The framework aims to reduce preparation time while ensuring mathematical accuracy, adherence to institutional visual identity, and preservation of the instructor's tacit pedagogical knowledge through explicit rules. Application Design: The solution comprises a six-phase pipeline that replaces ad-hoc prompt engineering with a systematic workflow, utilizing text-based interfaces and code-driven generation (LaTeX/Beamer for slides, Python for figures), governed by pedagogical constraints, contextual calibrations, and automated review cycles. Findings: Validated over one year across 8 modules and 28 project contexts in a Project-Based Learning environment, the architecture significantly reduced instructor workload. Generated assets underwent independent peer review and were deployed by six different faculty members, confirming scalability beyond a single author. Based on over 600 voluntary student evaluations, materials achieved high quality ratings from 8.5 to 9.9/10. Results indicate high reproducibility, minimized hallucinations, and sustained pedagogical and visual fidelity, suggesting viability for broad STEM educational applications.
