This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implementation. We discuss the benefits and limitations of AI-driven content generation, emphasizing the necessity of human oversight in ensuring the quality of educational materials. This work is elucidated through two detailed case studies where we applied GPT-4 in creating complex higher-order assessments and active learning components for different courses. From our experiences, we provide best practices for effectively using LLMs in instructional design tasks, such as utilizing templates, fine-tuning, handling unexpected output, implementing LLM chains, citing references, evaluating output, creating rubrics, grading, and generating distractors. We also share our vision of a future recommendation system, where a customized GPT-4 extracts instructional design principles from educational studies and creates personalized, evidence-supported strategies for users' unique educational contexts. Our research contributes to understanding and optimally harnessing the potential of AI-driven language models in enhancing educational outcomes.

翻译：本文系统探究了大规模语言模型（LLMs，特别是GPT-4）在教学设计领域的应用。聚焦于拓展循证教学设计专业能力，本研究旨在弥合教育理论研究与实践应用之间的鸿沟。我们探讨了人工智能驱动内容生成的优势与局限，强调在保障教育材料质量过程中人工监督的必要性。通过两个详细案例研究阐述本研究成果：分别将GPT-4应用于不同课程中复杂高阶评估与主动学习组件的构建。基于实践经验，我们总结出有效运用LLMs开展教学设计任务的最佳实践，包括模板运用、微调优化、异常输出处理、LLM链式调用、文献引用、输出评估、评价量规创建、评分实施及干扰项生成等环节。同时展望未来推荐系统的发展愿景：通过定制化GPT-4从教育研究中提取教学设计原则，为用户的个性化教育场景创建循证支持策略。本研究为理解并优化利用人工智能语言模型提升教育成效提供了理论依据与实践路径。