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：本文全面探讨了在教学设计领域中利用大型语言模型（LLMs），特别是GPT-4的应用。研究聚焦于扩展循证教学设计的专业知识，旨在弥合教育理论研究与实践应用之间的鸿沟。我们讨论了AI驱动内容生成的优势与局限，并强调人工监督在确保教育材料质量中的必要性。通过两项详细的案例研究加以阐述——分别应用GPT-4为不同课程创建复杂的高阶评估任务与主动学习组件。基于实践经验，我们总结了在教学设计任务中有效使用LLMs的最佳实践，包括模板化应用、微调、异常输出处理、LLM链实现、文献引用、输出评估、评估量规创建、评分及干扰项生成。我们还展望了未来推荐系统的愿景：通过定制化GPT-4从教育研究中提取教学设计原则，为用户独特的教育情境生成个性化且具有证据支持的教学策略。本研究有助于理解并最优化利用AI驱动的语言模型在提升教育成效方面的潜力。